3GPP TR 38.901 V19.1.0 (2025-09)

Technical Report

 

3rd Generation Partnership Project;

Technical Specification Group Radio Access Network;

Study on channel model for frequencies from 0.5 to 100 GHz

(Release 19)

    

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The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP.The present document has not been subject to any approval process by the 3GPP<br> Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP<br> only. The Organizational Partners accept no liability for any use of this Specification.Specifications and Reports for implementation of the 3GPP<br> TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.

 

 

 

 

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Contents #

1    Scope    8

2    References    8

3    Definitions, symbols and abbreviations    9

3.1    Definitions    9

3.2    Symbols    9

3.3    Abbreviations    10

4    Introduction    12

5    Void    13

6    Status/expectation of existing information on high frequencies    13

6.1    Channel modelling works outside of 3GPP    13

6.2    Scenarios of interest    15

6.3    Channel measurement capabilities    16

6.4    Modelling objectives    18

7    Channel model(s) for 0.5-100 GHz    18

7.1    Coordinate system    18

7.1.1    Definition    18

7.1.2    Local and global coordinate systems    19

7.1.3    Transformation from a LCS to a GCS    19

7.1.4    Transformation from an LCS to a GCS for downtilt angle only    23

7.2    Scenarios    24

7.3    Antenna modelling    28

7.3.0    Antenna array structure    28

7.3.1    Antenna port mapping    32

7.3.2    Polarized antenna modelling    32

7.4    Pathloss, LOS probability and penetration modelling    35

7.4.1    Pathloss    35

7.4.2    LOS probability    38

7.4.3    O2I penetration loss    39

7.4.3.1    O2I building penetration loss    39

7.4.3.2    O2I car penetration loss    41

7.4.4    Autocorrelation of shadow fading    41

7.5    Fast fading model    41

7.6    Additional modelling components    58

7.6.0    Introduction of additional modelling components    58

7.6.1    Oxygen absorption    58

7.6.2    Large bandwidth and large antenna array    59

7.6.2.1    Modelling of the propagation delay    59

7.6.2.2    Modelling of intra-cluster angular and delay spreads    60

7.6.3    Spatial consistency    61

7.6.3.1    Spatial consistency procedure    61

7.6.3.2    Spatially-consistent UT/BS mobility modelling    62

7.6.3.3    LOS/NLOS, indoor states and O2I parameters    66

7.6.3.4    Applicability of spatial consistency    67

7.6.4    Blockage    67

7.6.4.1    Blockage model A    68

7.6.4.2    Blockage model B    70

7.6.5    Correlation modelling for multi-frequency simulations    72

7.6.5.1    Alternative channel generation method    73

7.6.6    Time-varying Doppler shift    75

7.6.7    UT rotation    75

7.6.8    Explicit ground reflection model    75

7.6.9    Absolute time of arrival    78

7.6.10    Dual mobility    79

7.6.11    Sources of EM interference    79

7.6.12    Embedded devices    79

7.6.13    Near-field channel model    80

7.6.14    Spatial non-stationarity channel model    81

7.6.14.1    Spatial non-stationarity channel model at TRP side    81

7.6.14.1.1    Introduction    81

7.6.14.1.2    Physical blocker-based Model    82

7.6.14.1.3    Stochastic based Model    83

7.6.14.2    Spatial non-stationarity channel model at UT side    84

7.6.15    Number of cluster variability model    85

7.6.16    Polarization power variability model    85

7.7    Channel models for link-level evaluations    86

7.7.1    Clustered Delay Line (CDL) models    86

7.7.2    Tapped Delay Line (TDL) models    90

7.7.3    Scaling of delays    93

7.7.4    Spatial filter for generating TDL channel model    94

7.7.4.1    Exemplary filters/antenna patterns    94

7.7.4.2    Generation procedure    95

7.7.5    Extension for MIMO simulations    95

7.7.5.1    CDL extension: Scaling of angles    95

7.7.5.2    TDL extension: Applying a correlation matrix    97

7.7.6    K-factor for LOS channel models    97

7.8    Channel model calibration    98

7.8.1    Large scale calibration    98

7.8.2    Full calibration    100

7.8.3    Calibration of additional features    102

7.8.4    Calibration of the indoor factory scenario    110

7.9    Channel model(s) for ISAC    111

7.9.0    Introduction    111

7.9.1    Scenarios    112

7.9.2    Physical object model    117

7.9.2.0    Introduction    117

7.9.2.1    RCS of a sensing target    117

7.9.2.2    Cross-polarization matrix of a sensing target    119

7.9.3    Reference channel models and required updates    120

7.9.4    Fast fading model    125

7.9.4.0    Introduction    125

7.9.4.1    Target channel    127

7.9.4.2    Background channel    132

7.9.4.3    Combining target channel and background channel    134

7.9.5    Additional modelling components    134

7.9.5.0    Introduction    134

7.9.5.1    Spatial consistency    134

7.9.5.2    Type-2 environment object    135

7.9.5.3    Power normalization across target channel and background channel    139

7.9.5.4    Doppler of mobile scatterers    139

7.9.5.5    Lower power clusters    139

7.9.5.6    Blockage    140

7.9.6    Channel model calibration    140

7.9.6.1    Large scale calibration    140

7.9.6.2    Full calibration    144

7.9.6.3    Calibration of additional features    145

8    Map-based hybrid channel model (Alternative channel model methodology)    149

8.1    Coordinate system    149

8.2    Scenarios    149

8.3    Antenna modelling    149

8.4    Channel generation    149

Annex A:    Further parameter definitions    160

A.1    Calculation of angular spread    160

A.2    Calculation of mean angle    160

A.3    Calculation of cluster angular spread    160

A.4    Calculation of cluster mean angle    160

A.5    Calculation of scaling factor for changing CDL model angular spread    160

Annex B:    Change history    162

 

<br>Foreword #

This Technical Report has been produced by the 3rd Generation Partnership Project (3GPP).

The contents of the present document are subject to continuing work within the TSG and may change following formal TSG approval. Should the TSG modify the contents of the present document, it will be re-released by the TSG with an identifying change of release date and an increase in version number as follows:

Version x.y.z

where:

x    the first digit:

1    presented to TSG for information;

2    presented to TSG for approval;

3    or greater indicates TSG approved document under change control.

y    the second digit is incremented for all changes of substance, i.e. technical enhancements, corrections, updates, etc.

z    the third digit is incremented when editorial only changes have been incorporated in the document.

In the present document, modal verbs have the following meanings:

shall    indicates a mandatory requirement to do something

shall not    indicates an interdiction (prohibition) to do something

The constructions "shall" and "shall not" are confined to the context of normative provisions, and do not appear in Technical Reports.

The constructions "must" and "must not" are not used as substitutes for "shall" and "shall not". Their use is avoided insofar as possible, and they are not used in a normative context except in a direct citation from an external, referenced, non-3GPP document, or so as to maintain continuity of style when extending or modifying the provisions of such a referenced document.

should    indicates a recommendation to do something

should not    indicates a recommendation not to do something

may    indicates permission to do something

need not    indicates permission not to do something

The construction "may not" is ambiguous and is not used in normative elements. The unambiguous constructions "might not" or "shall not" are used instead, depending upon the meaning intended.

can    indicates that something is possible

cannot    indicates that something is impossible

The constructions "can" and "cannot" are not substitutes for "may" and "need not".

will    indicates that something is certain or expected to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document

will not    indicates that something is certain or expected not to happen as a result of action taken by an agency the behaviour of which is outside the scope of the present document

might    indicates a likelihood that something will happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document

might not    indicates a likelihood that something will not happen as a result of action taken by some agency the behaviour of which is outside the scope of the present document

In addition:

is    (or any other verb in the indicative mood) indicates a statement of fact

is not    (or any other negative verb in the indicative mood) indicates a statement of fact

The constructions "is" and "is not" do not indicate requirements.

<br>1     Scope #

The present document captures the findings of the study item, "Study on channel model for frequency spectrum above 6 GHz" [2] and from further findings of:

-    the study item, "Study on New Radio Access Technology [22]",

-    the study item "Study on Channel Modeling for Indoor Industrial Scenarios [23]",

-    the study item "New SID: Study on channel modelling enhancements for 7-24GHz for NR [24]",

-    and the study item "Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR [26]".

The channel models in the present document address the frequency range 0.5-100 GHz. The purpose of this TR is to help TSG RAN WG1 to properly model and evaluate the performance of physical layer techniques using the appropriate channel model(s). Therefore, the TR will be kept up-to-date via CRs in the future.

This document relates to the 3GPP evaluation methodology and covers the modelling of the physical layer of both Mobile Equipment and Access Network of 3GPP systems.

This document is intended to capture the channel model(s) for frequencies from 0.5GHz up to 100GHz.

2     References #

The following documents contain provisions which, through reference in this text, constitute provisions of the present document.

-    References are either specific (identified by date of publication, edition number, version number, etc.) or nonspecific.

-    For a specific reference, subsequent revisions do not apply.

-    For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.

[1]    3GPP TR 21.905: "Vocabulary for 3GPP Specifications".

[2]    3GPP TD RP-151606: "Study on channel model for frequency spectrum above 6 GHz".

[3]    3GPP TR 36.873 (V12.2.0): "Study on 3D channel model for LTE".

[4]    3GPP RP-151847: "Report of RAN email discussion about >6GHz channel modelling", Samsung.

[5]    3GPP TD R1-163408: "Additional Considerations on Building Penetration Loss Modelling for 5G System Performance Evaluation", Straight Path Communications.

[6]    ICT-317669-METIS/D1.4: "METIS channel model, METIS 2020, Feb, 2015".

[7]    Glassner, A S: "An introduction to ray tracing. Elsevier, 1989".

[8]    McKown, J. W., Hamilton, R. L.: "Ray tracing as a design tool for radio networks, Network, IEEE, 1991(6): 27-30".

[9]    Kurner, T., Cichon, D. J., Wiesbeck, W.: "Concepts and results for 3D digital terrain-based wave propagation models: An overview", IEEE J.Select. Areas Commun., vol. 11, pp. 1002–1012, 1993.

[10]    Born, M., Wolf, E.: "Principles of optics: electromagnetic theory of propagation, interference and diffraction of light", CUP Archive, 2000.

[11]    Friis, H.: "A note on a simple transmission formula", proc. IRE, vol. 34, no. 5, pp. 254–256, 1946.

[12]    Kouyoumjian, R.G., Pathak, P.H.: "A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface" Proc. IEEE, vol. 62, pp. 1448–1461, Nov. 1974.

[13]    Pathak, P.H., Burnside, W., Marhefka, R.: "A Uniform GTD Analysis of the Diffraction of Electromagnetic Waves by a Smooth Convex Surface", IEEE Transactions on Antennas and Propagation, vol. 28, no. 5, pp. 631–642, 1980.

[14]    IST-WINNER II Deliverable 1.1.2 v.1.2, "WINNER II Channel Models", IST-WINNER2, Tech. Rep., 2007 (http://www.ist-winner.org/deliverables.html).

[15]    3GPP TR36.101: "User Equipment (UE) radio transmission and reception".

[16]    3GPP TR36.104: "Base Station (BS) radio transmission and reception".

[17]    Asplund, H., Medbo, J., Göransson, B., Karlsson, J., Sköld, J.: "A simplified approach to applying the 3GPP spatial channel model", in Proc. of PIMRC 2006.

[18]    ITU-R Rec. P.1816: "The prediction of the time and the spatial profile for broadband land mobile services using UHF and SHF bands".

[19]    ITU-R Rec. P.2040-1: "Effects of building materials and structures on radiowave propagation above about 100 MHz", International Telecommunication Union Radiocommunication Sector ITU-R, 07/2015.

[20]    ITU-R Rec. P.527-3: "Electrical characteristics of the surface of the earth", International Telecommunication Union Radiocommunication Sector ITU-R, 03/1992.

[21]    Jordan, E.C., Balmain, K.G.: "Electromagnetic Waves and Radiating Systems", Prentice-Hall Inc., 1968.

[22]    3GPP TD RP-162469: "Study on New Radio (NR) Access Technology".

[23]    3GPP TD RP-182138: "SID on Channel Modeling for Indoor Industrial Scenarios".

[24]    3GPP TD RP-234018: "New SID: Study on channel modelling enhancements for 7-24GHz for NR"

[25]    3GPP TD R1-2504960: "Data source descriptions for 7 – 24 GHz SI".

[26]     3GPP TD RP-242348: "Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR".

[ 27]    3GPP TD R1-2504948: "Information on validations for ISAC"

 

3     Definitions, symbols and abbreviations #

3.1     Definitions #

For the purposes of the present document, the terms and definitions given in TR 21.905 [1] apply.

3.2     Symbols #

For the purposes of the present document, the following symbols apply:

Image    antenna radiation power pattern

Image    maximum attenuation

d2D    2D distance between Tx and Rx

d3D    3D distance between Tx and Rx

Image    antenna element spacing in horizontal direction

Image    antenna element spacing in vertical direction

f    frequency

fc    center frequency / carrier frequency

Frx,u,θ    Receive antenna element u field pattern in the direction of the spherical basis vector Image

Frx,u,ϕ    Receive antenna element u field pattern in the direction of the spherical basis vector Image

Ftx,s,θ    Transmit antenna element s field pattern in the direction of the spherical basis vector Image

Frx,s,ϕ    Transmit antenna element s field pattern in the direction of the spherical basis vector Image

hBS    antenna height for BS

hUT    antenna height for UT

Image    spherical unit vector of cluster n, ray m, for receiver

Image    spherical unit vector of cluster n, ray m, for transmitter

α    bearing angle

β    downtilt angle

γ    slant angle

Image    wavelength

κ    cross-polarization power ratio in linear scale

μlgASA    mean value of 10-base logarithm of azimuth angle spread of arrival

μlgASD    mean value of 10-base logarithm of azimuth angle spread of departure

μlgDS    mean value of 10-base logarithm of delay spread

μlgZSA    mean value of 10-base logarithm of zenith angle spread of arrival

μlgZSD    mean value of 10-base logarithm of zenith angle spread of departure

Image    LOS probability

Image    side-lobe attenuation in vertical direction

σlgASA    standard deviation of 10-base logarithm of azimuth angle spread of arrival

σlgASD    standard deviation of 10-base logarithm of azimuth angle spread of departure

σlgDS    standard deviation value of 10-base logarithm of delay spread

σlgZSA    standard deviation of 10-base logarithm of zenith angle spread of arrival

σlgZSD    standard deviation of 10-base logarithm of zenith angle spread of departure

Image    standard deviation of SF

Image    azimuth angle

Image    zenith angle

Image    spherical basis vector (unit vector) for GCS

Image    spherical basis vector (unit vector) for LCS

Image    horizontal 3 dB beamwidth of an antenna

Image    spherical basis vector (unit vector), orthogonal to Image, for GCS

Image    spherical basis vector (unit vector), orthogonal to Image, for LCS

Image    electrical steering angle in vertical direction

Image    vertical 3 dB beamwidth of an antenna

ψ    Angular displacement between two pairs of unit vectors

 

3.3     Abbreviations #

For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].

2D    two-dimensional

3D    three-dimensional

AGV    Automated Guided Vehicles

AOA    Azimuth angle Of Arrival

AOD    Azimuth angle Of Departure

AS    Angular Spread

ASA    Azimuth angle Spread of Arrival

ASD    Azimuth angle Spread of Departure

BF    Beamforming

BS    Base Station

BP    Breakpoint

BW    Beamwidth

CDF    Cumulative Distribution Function

CDL    Clustered Delay Line

CRS    Common Reference Signal

D2D    Device-to-Device

DFT    Discrete Fourier Transform

DS    Delay Spread

EO    Environment Object

GCS    Global Coordinate System

IID    Independent and identically distributed

InF    Indoor Factory

InF-SL    Indoor Factory with Sparse clutter and Low base station height (both Tx and Rx are below the average height of the clutter)

InF-DL    Indoor Factory with Dense clutter and Low base station height (both Tx and Rx are below the average height of the clutter)

InF-SH    Indoor Factory with Sparse clutter and High base station height (Tx or Rx elevated above the clutter)

InF-DH    Indoor Factory with Dense clutter and High base station height (Tx or Rx elevated above the clutter)

InF-HH    Indoor Factory with High Tx and High Rx (both elevated above the clutter)

InH    Indoor Hotspot

IRR    Infrared Reflecting

ISAC    Integrated Sensing and Communication

ISD    Intersite Distance

K    Ricean K factor

LCS    Local Coordinate System

LOS    Line Of Sight

MIMO    Multiple-Input-Multiple-Output

MPC    Multipath Component

NLOS    Non-LOS

O2I    Outdoor-to-Indoor

O2O    Outdoor-to-Outdoor

OFDM    Orthogonal Frequency-Division Multiplexing

PAS    Power angular spectrum

PL    Path Loss

PRB    Physical Resource Block

RCS    Radar cross-section

RMa    Rural Macro

RMS    Root Mean Square

RP    Reference Point

RSRP    Reference Signal Received Power

Rx    Receiver

SCM    Spatial Channel Model

SINR    Signal-to-Interference-plus-Noise Ratio

SIR    Signal-to-Interference Ratio

SSCM    Statistical Spatial Channel Model

SF    Shadow Fading

SLA    Sidelobe Attenuation

SPST    Scattering Point of a ST

SRX    Sensing Receiver

ST    Sensing Target

STX    Sensing Transmitter

TDL    Tapped Delay Line

TOA    Time Of Arrival

TRP    Transmission Reception Point

Tx    Transmitter

UAV    Unmanned Aerial Vehicle

UMa    Urban Macro

UMi    Urban Micro

UT    User Terminal

UTD    Uniform Theory of Diffraction

V2V    Vehicle-to-Vehicle

XPR    Cross-Polarization Ratio

ZOA    Zenith angle Of Arrival

ZOD    Zenith angle Of Departure

ZSA    Zenith angle Spread of Arrival

ZSD    Zenith angle Spread of Departure

 

4     Introduction #

At TSG RAN #69 meeting the Study Item Description on "Study on channel model for frequency spectrum above 6 GHz" was approved [2]. This study item covers the identification of the status/expectation of existing information on high frequencies (e.g. spectrum allocation, scenarios of interest, measurements, etc), and the channel model(s) for frequencies up to 100 GHz. This technical report documents the channel model(s). The new channel model has to a large degree been aligned with earlier channel models for <6 GHz such as the 3D SCM model (TR 36.873) or IMT-Advanced (ITU-R M.2135). The new model supports comparisons across frequency bands over the range 0.5-100 GHz. The modelling methods defined in this technical report are generally applicable over the range 0.5-100 GHz, unless explicitly mentioned otherwise in this technical report for specific modelling method, involved parameters and/or scenario.

Subsequently, at the TSG RAN #81 meeting the Study Item Description "Study on Channel Modeling for Indoor Industrial Scenarios" was approved [23]. The findings from this study item is also captured in the present technical report. The Industrial channel model was developed by considering new measurements and information in the literature. An overview list of all such contributions and sources is available in tdoc R1-1909706.

Subsequently, at the TSG RAN #102 meeting the Study Item Description "Study on channel modelling enhancements for 7-24GHz for NR" was approved [24]. The findings from this study are also captured in the present technical report. Channel modeling was further updated by considering new measurements and information in the literature. An overview of the information sources is available in tdoc R1-2504960 [25].

At TSG RAN #102 meeting the Study Item Description “Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR” was approved. The findings from this study item are captured in Clause 7.9. The ISAC channel model was developed considering new measurements and information in the literature. The RCS validation results are obtained based on certain object sizes. An overview list of the sources is available in [27].

The channel model is applicable for link and system level simulations in the following conditions:

-    For system level simulations, supported scenarios are urban microcell street canyon, urban macrocell, indoor office, rural macrocell, indoor factory, and suburban macrocell.

-    Bandwidth is supported up to 10% of the center frequency but no larger than 2GHz.

-    Mobility of either one end of the link or both ends of the link is supported

-    For the stochastic model, spatial consistency is supported by correlation of LSPs and SSPs as well as LOS/NLOS state.

-    Large array support is based on far field assumption and stationary channel over the size of the array.

-    Near field channel modeling (i.e., characteristics of spherical wavefront) and spatial non-stationarity (i.e., antenna element-wise power variation) are supported.

-    The sensing target is assumed in the far field of the sensing transmitter/receiver for the channel model(s) for ISAC in Clause 7.9.

 

5     Void #

 

6     Status/expectation of existing information on high frequencies #

6.1     Channel modelling works outside of 3GPP #

This clause summarizes the channel modelling work outside of 3GPP based on the input from companies.

Groups and projects with channel models:

-    METIS (Mobile and wireless communications Enablers for the Twenty-twenty Information Society)

-    MiWEBA (Millimetre-Wave Evolution for Backhaul and Access)

-    ITU-R M

-    COST2100

-    IEEE 802.11

-    NYU WIRELESS: interdisciplinary academic research center

-    Fraunhofer HHI has developed the QuaDRiGa channel model, Matlab implementation is available at http://quadriga-channel-model.de

Groups and projects which intend to develop channel models:

-    5G mmWave Channel Model Alliance: NIST initiated, North America based

-    mmMAGIC (Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications): Europe based

-    IMT-2020 5G promotion association: China based

METIS Channel Models:

-    Identified 5G requirements (e.g., wide frequency range, high bandwidth, massive MIMO, 3-D and accurate polarization modelling)

-    Performed channel measurements at various bands between 2GHz and 60 GHz

-    Provided different channel model methodologies (map-based model, stochastic model or hybrid model). For stochastic model, the proposed channel is focused on outdoor square, Indoor cafeteria and indoor shopping mall scenarios.

MiWEBA Channel Models:

-    Addressed various challenges: Shadowing, spatial consistency, environment dynamics, spherical wave modelling, dual mobility Doppler model, ratio between diffuse and specular reflections, polarization

-    Proposed Quasi-deterministic channel model

-    Performed channel measurements at 60 GHz

-    Focused on university campus, street canyon, hotel lobby, backhaul, and D2D scenarios.

ITU-R M Channel Models:

-    Addressed the propagation loss and atmospheric loss on mmW

-    Introduced enabling antenna array technology and semiconductor technology

-    Proposed deployment scenarios, focused on dense urban environment for high data rate service: indoor shopping mall, indoor enterprise, in home, urban hotspot in a square/street, mobility in city.

COST2100 and COST IC1004 Channel Models:

-    Geometry-based stochastic channel model that reproduce the stochastic properties of MIMO channels over time, frequency and space. It is a cluster-level model where the statistics of the large scale parameters are always guaranteed in each series of channel instances.

NYU WIRELESS Channel Models:

-    Conducted many urban propagation measurements on 28/38/60/73 GHz bands for both outdoor and indoor channels, measurements are continuing.

-    Proposed 3 areas for 5G mmWave channel modelling which are small modifications or extensions from 3GPP's current below 6GHz channel models

-    1) LOS/NLOS/blockage modelling (a squared exponential term); 2). Wideband power delay profiles (time clusters and spatial lobes for a simple extension to the existing 3GPP SSCM model); 3). Physics-based path loss model (using the existing 3GPP path loss equations, but simply replacing the "floating" optimization parameter with a deterministic 1 m "close-in" free space reference term in order to provide a standard and stable definition of "path loss exponent" across all different parties, scenarios, and frequencies).

802.11 ad/ay Channel Models:

-    Conducted ray-tracing methodology on 60 GHz band indoor channels, including conference room, cubicle, living room scenarios

-    Intra cluster parameters were proposed in terms of ray excess delay and ray power distribution

-    Human blockage models were proposed in terms of blockage probability and blockage attenuation

5G mmWave Channel Model Alliance:

-    Will provide a venue to promote fundamental research into measurement, analysis, identification of physical parameters, and statistical representations of mmWave propagation channels. 

-    Divided into six collaborative working groups that include a Steering Committee; Modelling Methodology Group; Measurement Methodology Group; and groups that focus on defining and parameterizing Indoor, Outdoor, and Emerging Usage Scenarios. 

-    Sponsored by Communications Technology Research Laboratory within the NIST.

mmMAGIC:

-    Brings together major infrastructure vendors, major European operators, leading research institutes and universities, measurement equipment vendors and one SME.

-    Will undertake extensive radio channel measurements in the 6-100 GHz range.

-    Will develop and validate advanced channel models that will be used for rigorous validation and feasibility analysis of the proposed concepts and system, as well as for usage in regulatory and standards fora.

IMT-2020 5G promotion association

-    Jointly established by three ministries of China based on the original IMT-Advanced promotion group

-    Members including the main operators, vendors, universities and research institutes in China

-    The major platform to promote 5G technology research in China and to facilitate international communication and cooperation

QuaDRiGa (Fraunhofer HHI)

-    QuaDRiGa (QUAsi Deterministic RadIo channel GenerAtor) was developed at the Fraunhofer Heinrich Hertz Institute within the Wireless Communications and Networks Department to enable the modelling of MIMO radio channels for specific network configurations, such as indoor, satellite or heterogeneous configurations.

-    Besides being a fully-fledged 3D geometry-based stochastic channel model (well aligned with TR36.873), QuaDRiGa contains a collection of features created in SCM(e) and WINNER channel models along with novel modelling approaches which provide features to enable quasi-deterministic multi-link tracking of users (receiver) movements in changing environments. QuaDRiGa supports Massive MIMO modelling enabled through a new multi-bounce scattering approach and spherical wave propagation. It will be continuously extended with features required by 5G and frequencies beyond 6 GHz. The QuaDRiGa model is supported by data from extensive channel measurement campaigns at 10 / 28 / 43 / 60 / 82 GHz performed by the same group.

ATIS Next G Alliance

-    Published a report that includes a collection of results from several measurement campaigns relevant to deployment scenarios and use cases for 7-24 GHz channel modeling enhancements and channel modeling for integrated sensing and communications.

ETSI Industry specification group (ISG) Integrated sensing and communications (ISAC)

-    ETSI ISG ISAC plans to publish Group Report (GR) /ISC-002 that provides information on advanced ISAC channel modeling concepts and their validation through measurement, in addition to information on evaluation methodology framework, deployment scenarios and the corresponding potentially suitable frequency bands.

6.2     Scenarios of interest #

Brief description of the key scenarios of interest identified (see note):

(1)    UMi (Street canyon, open area) with O2O and O2I: This is similar to 3D-UMi scenario, where the BSs are mounted below rooftop levels of surrounding buildings. UMi open area is intended to capture real-life scenarios such as a city or station square. The width of the typical open area is in the order of 50 to 100 m.

    Example: [Tx height:10 m, Rx height with reference to floor height: 1.5 m, ISD: 200 m]

(2)    UMa with O2O and O2I: This is similar to 3D-UMa scenario, where the BSs are mounted above rooftop levels of surrounding buildings.

    Example: [Tx height:25 m, Rx height with reference to floor height: 1.5 m, ISD: 200 m, 500 m]

(3)    Indoor: This scenario is intended to capture various typical indoor deployment scenarios, including office environments, and shopping malls. The typical office environment is comprised of open cubicle areas, walled offices, open areas, corridors etc. The BSs are mounted at a height of 2-3 m either on the ceilings or walls. The shopping malls are often 1-5 stories high and may include an open area (or "atrium") shared by several floors. The BSs are mounted at a height of approximately 3 m on the walls or ceilings of the corridors and shops.

    Example: [Tx height: 2-3 m, Rx height: 1.5 m, area: 500 square meters]

(4)    Backhaul, including outdoor above roof top backhaul in urban area and street canyon scenario where small cell BSs are placed at lamp posts.

(5)    D2D/V2V. Device-to-device access in open area, street canyon, and indoor scenarios. V2V is a special case where the devices are mobile.

(6)    Other scenarios such as Stadium (open-roof) and Gym (close-roof).

(7)    Indoor industrial scenarios

(8)    Suburban macro (SMa) scenarios: In suburban macro-cells base stations are located above the surrounding environment to allow wide area coverage, and mobile stations are outdoors at street level and within commercial and residential buildings.

    Example: [Tx height: 35 m, Rx height with reference to floor height: 1.5 m, ISD: 1299 m, 1732 m]

(9)    Integrated sensing and communication scenarios as described in Clause 7.9.1.

Note:    The scenarios of interest are based on the plenary email discussion and different from the supported scenarios in clause 7. The indoor industrial scenarios were identified at a later stage in the TSG RAN #81 meeting. SMa scenarios were identified at a later stage as part of Study on channel modelling enhancements for 7-24GHz for NR. The integrated sensing and communication scenarios were identified at a later stage as part of Study on channel modelling for integrated sensing and communications for NR.

6.3     Channel measurement capabilities #

The measurement capability as reported by each company is summarized in the following table.

Table 6.3-1: Channel measurement capabilities

 

6 - 20 GHz

20 - 30 GHz

30 - 60 GHz

>60 GHz

Urban macro

CMCC

Nokia/Aalborg

Ericsson

Vodafone

BT

Apple

BUPT

AT&T

NTT Docomo

Huawei

Nokia/Aalborg

Ericsson

 

NYU

Ericsson

 

 

Urban micro

Aalto University

CMCC

Ericsson

Intel/Fraunhofer HHI

Nokia/Aalborg

NTT DOCOMO

Orange

NYU/Sharp

Apple

BUPT

AT&T

Samsung

Keysight

Huawei

Southeast University/ Purple Mountain Laboratories/China Telecom

AT&T

Aalto University

CMCC

Huawei

Intel/Fraunhofer HHI

Nokia/Aalborg

NTT DOCOMO

NYU

Qualcomm

Samsung

CATT

KT

ETRI

ITRI/CCU

ZTE

Ericsson

AT&T

Huawei

Intel/Fraunhofer HHI

NTT DOCOMO

Qualcomm

CATT

ETRI

ITRI/CCU

ZTE

Ericsson

AT&T

Aalto University

Huawei

Intel/Fraunhofer HHI

NYU

Suburban macro

AT&T

Ericsson

Vodafone

Apple

Huawei

Ericsson

Nokia

Ericsson

 

Indoor

Aalto University

CMCC

Ericsson

Huawei

Intel/Fraunhofer HHI

Nokia/Aalborg

NTT DOCOMO

Orange

NYU/Sharp

Apple

Sony

ZTE

BUPT

AT&T

AT&T

Alcatel-Lucent

Aalto University

BUPT

CMCC

Huawei

Intel/Fraunhofer HHI

Nokia/Aalborg

NTT DOCOMO

NYU

Qualcomm

Samsung

CATT

KT

ETRI

ITRI/CCU

ZTE

AT&T

Ericsson

Huawei

Intel/Fraunhofer HHI

NTT DOCOMO

NYU

Qualcomm

CATT

ETRI

ITRI/CCU

ZTE

AT&T

Aalto University

Huawei

Intel/Fraunhofer HHI

NYU

O2I

Ericsson

Huawei

Intel/Fraunhofer HHI

Nokia/Aalborg

NTT DOCOMO

Orange

BT

Vodafone

AT&T

Alcatel-Lucent

Ericsson

Huawei

Intel/Fraunhofer HHI

NTT DOCOMO

NYU

Samsung

KT

Nokia

AT&T

Ericsson

Huawei

Intel/Fraunhofer HHI

NTT DOCOMO

AT&T

Huawei

Intel/Fraunhofer HHI

 

6.4     Modelling objectives #

The requirements for channel modelling are as follows.

-    Channel model SI should take into account the outcome of RAN-level discussion in the '5G' requirement study item.

-    Complexity in terms of Description, Generating channel coefficients, development complexity and Simulation time should be considered.

-    Support frequency range up to 100 GHz.

-    The critical path of the SI is 6 – 100 GHz.

-    Take care of mmW propagation aspects such as blocking and atmosphere attenuation.

-    The model should be consistent in space, time and frequency.

-    Support large channel bandwidths (up to 10% of carrier frequency).

-    Aim for the channel model to cover a range of coupling loss considering current typical cell sizes, e.g. up to km-range macro cells. Note: This is to enable investigation of the relevance of the 5G system using higher frequency bands to existing deployments.

-    Accommodate UT mobility

-    Mobile speed up to 500 km/h.

-    Develop a methodology considering that model extensions to D2D and V2V may be developed in future SI.

-    Support large antenna arrays.

-    Support near-field channel propagation (i.e., characteristics of spherical wavefront) and spatial non-stationarity (i.e. antenna element-wise power variation).

-    Support integrated sensing and communications

7     Channel model(s) for 0.5-100 GHz #

7.1     Coordinate system #

7.1.1     Definition #

A coordinate system is defined by the x, y, z axes, the spherical angles and the spherical unit vectors as shown in Figure 7.1.1. Figure 7.1.1 defines the zenith angle Image and the azimuth angleImage in a Cartesian coordinate system. Note that Imagepoints to the zenith and Imagepoints to the horizon. The field component in the direction of Image is given by Imageand the field component in the direction of Image is given by Image.

Image

Figure 7.1.1: Definition of spherical angles and spherical unit vectors in a Cartesian coordinate system, whereImageis the given direction, Image and Imageare the spherical basis vectors

 

7.1.2     Local and global coordinate systems #

A Global Coordinate System (GCS) is defined for a system comprising multiple BSs and UTs. An array antenna for a BS or a UT can be defined in a Local Coordinate System (LCS). An LCS is used as a reference to define the vector far-field that is pattern and polarization, of each antenna element in an array. It is assumed that the far-field is known in the LCS by formulae. The placement of an array within the GCS is defined by the translation between the GCS and a LCS. The orientation of the array with respect to the GCS is defined in general by a sequence of rotations (described in clause 7.1.3). Since this orientation is in general different from the GCS orientation, it is necessary to map the vector fields of the array elements from the LCS to the GCS. This mapping depends only on the orientation of the array and is given by the equations in clause 7.1.3. Note that any arbitrary mechanical orientation of the array can be achieved by rotating the LCS with respect to the GCS.

7.1.3     Transformation from a LCS to a GCS #

A GCS with coordinates (x, y, z, Image, Image) and unit vectors (Image, Image) and an LCS with "primed" coordinates (x', y', z', Image, Image) and "primed" unit vectors (Image, Image) are defined with a common origins in Figures 7.1.3-1 and 7.1.3-2. Figure 7.1.3-1 illustrates the sequence of rotations that relate the GCS (gray) and the LCS (blue). Figure 7.1.3-2 shows the coordinate direction and unit vectors of the GCS (gray) and the LCS (blue). Note that the vector fields of the array antenna elements are defined in the LCS. In Figure 7.1.3-1 we consider an arbitrary 3D-rotation of the LCS with respect to the GCS given by the angles α, β, γ. The set of angles α, β, γ can also be termed as the orientation of the array antenna with respect to the GCS.

Note that the transformation from a LCS to a GCS depends only on the angles α, β, γ. The angle α is called the bearing angle, β is called the downtilt angle and γ is called the slant angle.

 

Image

Image

Figure 7.1.3-1: Orienting the LCS (blue) with respect to the GCS (gray) by a sequence of 3 rotations: α, β, γ.

Figure 7.1.3-2: Definition of spherical coordinates and unit vectors in both the GCS and LCS.

Let Imagedenote an antenna element pattern in the LCS and Imagedenote the same antenna element pattern in the GCS. Then the two are related simply by

    Image    (7.1-1)

with Image and Image given by (7.1-7) and (7.1-8).

Let us denote the polarized field components in the LCS by Image, Imageand in the GCS by Image, Image. Then they are related by equation (7.1-11).

Any arbitrary 3D rotation can be specified by at most 3 elemental rotations, and following the framework of Figure 7.1.3-1, a series of rotations about the z, Imageand Imageaxes are assumed here, in that order. The dotted and double-dotted marks indicate that the rotations are intrinsic, which means that they are the result of one () or two () intermediate rotations. In other words, the Image axis is the original y axis after the first rotation about z, and the Image axis is the original x axis after the first rotation about z and the second rotation aboutImage. A first rotation of α about z sets the antenna bearing angle (i.e. the sector pointing direction for a BS antenna element). The second rotation of β about Image sets the antenna downtilt angle. Finally, the third rotation of γ about Image sets the antenna slant angle. The orientation of the x, y and z axes after all three rotations can be denoted as Image, Image and Image. These triple-dotted axes represents the final orientation of the LCS, and for notational purposes denoted as the x', y' and z' axes (local or "primed" coordinate system).

In order to establish the equations for transformation of the coordinate system and the polarized antenna field patterns between the GCS and the LCS, it is necessary to determine the composite rotation matrix that describes the transformation of point (x, y, z) in the GCS into point (x', y', z') in the LCS. This rotation matrix is computed as the product of three elemental rotation matrices. The matrix to describe rotations about the z,Imageand Image axes by the angles α, β and γ respectively and in that order is defined as

    Image    (7.1-2)

The reverse transformation is given by the inverse of R, which is also equal to the transpose of R since it is orthogonal.

    Image    (7.1-3)

The simplified forward and reverse composite rotation matrices are given by

    Image    (7.1-4)

and

    Image    (7.1-5)

These transformations can be used to derive the angular and polarization relationships between the two coordinate systems.

In order to establish the angular relationships, consider a point (x, y, z) on the unit sphere defined by the spherical coordinates (ρ=1, θ, φ), where ρ is the unit radius, θ is the zenith angle measured from the +z-axis, and φ is the azimuth angle measured from the +x-axis in the x-y plane. The Cartesian representation of that point is given by

    Image    (7.1-6)

The zenith angle is computed as Image and the azimuth angle as Image, where Image, Image and Image are the Cartesian unit vectors. If this point represents a location in the GCS defined by θ and φ, the corresponding position in the LCS is given by Image, from which local angles θ' and φ' can be computed. The results are given in equations (7.1-7) and (7.1-8).

    Image    (7.1-7)

    Image    (7.1-8)

These formulae relate the spherical angles (θ, φ) of the GCS to the spherical angles (θ', φ') of the LCS given the rotation operation defined by the angles (α, β, γ).

Let us denote the polarized field componentsImage, Image in the GCS andImage, Image in the LCS. These are related by

    Image    (7.1-9)

In this equation, Image and Image represent the spherical unit vectors of the GCS, and Image and Imageare the representations in the LCS. The forward rotation matrix R transforms the LCS unit vectors into the GCS frame of reference. These pairs of unit vectors are orthogonal and can be represented as shown in Figure 7.1.3-3.

 

Image

Figure 7.1.3-3: Rotation of the spherical basis vectors by an angle ψ due to the orientation of the LCS with respect to the GCS

Assuming an angular displacement of ψ between the two pairs of unit vectors, the rotation matrix of equation (7.1-9) can be further simplified as:

    Image    (7.1-10)

and equation (7.1-9) can be written as:

    Image    (7.1-11)

The angle ψ can be computed in numerous ways from equation (7.1-10), with one such way approach being

    Image    (7.1-12)

The dot products are readily computed using the Cartesian representation of the spherical unit vectors. The general expressions for these unit vectors are given by

    Image    (7.1-13)

and

    Image    (7.1-14)

The angle ψ can be expressed as a function of mechanical orientation (α, β, γ) and spherical position (θ, φ), and is given by

    Image    (7.1-15)

It can be shown thatImageandImagecan be expressed as:

    Image    (7.1-16)

    Image    (7.1-17)

 

7.1.4     Transformation from an LCS to a GCS for downtilt angle only #

In this clause equations are provided for the transformation from LCS to GCS assuming that the orientation of the LCS (with respect to the GCS) is such that the bearing angle α=0, the downtilt angle β is non-zero and the slant angle γ=0. In other words the y'-axis of the LCS is parallel to the y-axis of the GCS. Considering a BS antenna element the x-axis of the GCS is aligned with the pointing direction of the sector. Mechanical downtilt is modelled as a rotation of the LCS around the y-axis. For zero mechanical downtilt the LCS coincides with the GCS.

This transformation relates the spherical angles (Image,Image) in the global coordinate system to spherical angles (Image,Image) in the local (antenna-fixed) coordinate system and is defined as follows:

    Image    (7.1-18)

    Image    (7.1-19)

where Image is the mechanical tilt angle around the y-axis as defined in Figure 7.1.4. Note that the equations (7.1-7), (7.1-8) reduce to equations (7.1-18), (7.1-19) if both α and γ are zero.

The antenna element pattern Imagein the GCS is related to the antenna element pattern Imagein the LCS by the relation

    Image    (7.1-20)

with Image and Imagegiven by (7.1-18) and (7.1-19).

ImageFigure 7.1.4: Definition of angles and unit vectors when the LCS has been rotated an angle Image around the y-axis of the GCS

For a mechanical tilt angle Image, the global coordinate system field components Image and Image, are calculated from the field components Image and Image of the radiation pattern in the local (antenna-fixed) coordinate system as:

    Image    (7.1-21)

    Image    (7.1-22)

where Image and Image are defined as in (7.1-18) and (7.1-19), and Image is defined as:

    Image.    (7.1-23)

Note that the equation (7.1-15) is reduced to equation (7.1-23) if both α and γ are zero.

As an example, in the horizontal cut, i.e., for Image, equations (7.1-18), (7.1-19) and (7.1-23) become

    Image    (7.1-24)

    Image    (7.1-25)

    Image    (7.1-26)

 

7.2     Scenarios #

The detailed scenario description in this clause can be used for channel model calibration.

UMi-street canyon and UMa

Details on UMi-street canyon and UMa scenarios are listed in Table 7.2-1.

Table 7.2-1: Evaluation parameters for UMi-street canyon and UMa scenarios

Parameters

UMi - street canyon

UMa

Cell layout

Hexagonal grid, 19 micro sites, 3 sectors per site (ISD = 200 m)

Hexagonal grid, 19 macro sites, 3 sectors per site (ISD = 500 m, see note 1)

BS antenna height hBS

10 m

25 m

UT location

Outdoor/indoor

Outdoor and indoor

Outdoor and indoor

LOS/NLOS

LOS and NLOS

LOS and NLOS

Height hUT

Same as 3D-UMi in TR36.873

Same as 3D-UMa in TR36.873

Indoor UT ratio

80%

80%

UT mobility (horizontal plane only)

3 km/h

3 km/h

Min. BS - UT distance (2D)

10 m

35 m

UT distribution (horizontal)

Uniform

Uniform

NOTE 1:     UMa scenarios with ISDs between 200-500m can be used for evaluations.

 

Indoor-office

Details on indoor-office scenarios are listed in Table 7.2-2 and presented in Figure 7.2-1. More details, if necessary, can be added to Figure 7.2-1.

Table 7.2-2: Evaluation parameters for indoor-office scenarios

Parameters

Indoor - office

open office

Indoor - office

mixed office

Layout

Room size

(W x L x H)

120 m x 50 m x 3 m

ISD

20 m

BS antenna height hBS

3 m (ceiling)

UT location

LOS/NLOS

LOS and NLOS

 

Height hUT

1 m

UT mobility (horizontal plane only)

3 km/h

Min. BS - UT distance (2D)

0

UT distribution (horizontal)

Uniform

Note:    The only difference between the open office and mixed office models in this TR is the line of sight probability.

 

Image

Figure 7.2-1: Layout of indoor office scenarios.

 

RMa

The rural deployment scenario focuses on larger and continuous coverage. The key characteristics of this scenario are continuous wide area coverage supporting high speed vehicles. This scenario will be noise-limited and/or interference-limited, using macro TRPs. Details of RMa scenario is described in Table 7.2-3.

Table 7.2-3: Evaluation parameters for RMa

Parameters

RMa

Carrier Frequency

Up to 7 GHz

BS height hBS

35 m

Layout

Hexagonal grid, 19 Macro sites, 3 sectors per site, ISD = 1732 m or 5000 m

UT height hUT

1.5 m

UT distribution

Uniform

Indoor/Outdoor

50% indoor and 50% in car

LOS/NLOS

LOS and NLOS

Min BS - UT distance(2D)

35 m

 

Indoor Factory (InF)

The indoor factory (InF) scenario focuses on factory halls of varying sizes and with varying levels of density of "clutter", e.g. machinery, assembly lines, storage shelves, etc. Details of the InF scenario are listed in Table 7.2-4.

Table 7.2-4: Evaluation parameters for InF

 

InF

Parameters

InF-SL

(sparse clutter, low BS)

InF-DL

(dense clutter, low BS)

InF-SH

(sparse clutter, high BS)

InF-DH

(dense clutter, high BS)

InF-HH

(high Tx, high Rx)

Layout

Room size

Rectangular: 20-160000 m2

Ceiling height

5-25 m

5-15 m

5-25 m

5-15 m

5-25 m

 

Effective clutter height hc

< Ceiling height, 0-10 m

 

External wall and ceiling type

Concrete or metal walls and ceiling with metal-coated windows

Clutter type

Big machineries composed of regular metallic surfaces.

For example: several mixed production areas with open spaces and storage/commissioning areas

Small to medium metallic machinery and objects with irregular structure.

For example: assembly and production lines surrounded by mixed small-sized machineries.

Big machineries composed of regular metallic surfaces.

For example: several mixed production areas with open spaces and storage/commissioning areas

Small to medium metallic machinery and objects with irregular structure.

For example: assembly and production lines surrounded by mixed small-sized machineries.

Any

Typical clutter size, dclutter

10 m

2 m

10 m

2 m

Any

Clutter density r (percentage of surface area occupied by clutter)

Low clutter density

(<40%)

High clutter density

(≥40%)

Low clutter density

(<40%)

High clutter density

(≥40%)

Any

BS antenna height hBS

Clutter-embedded, i.e. the BS antenna height is below the average clutter height

Above clutter

Above clutter

UT location

LOS/NLOS

LOS and NLOS

100% LOS

Height hUT

Clutter-embedded

Above clutter

 

SMa

In suburban macro-cells base stations are located above the surrounding environment to allow wide area coverage, and mobile stations are outdoors at street level and within commercial and residential buildings. Buildings are typically low residential detached houses with one or two floors, or blocks of apartments/condos or commercial buildings with a few floors. Occasional open areas such as parks or playgrounds between the houses make the environment rather open. Streets do not form urban-like regular strict grid structure. In suburban areas, vegetation is more prevalent than in urban areas with a high variability of foliage density across different suburban areas. Details on SMa scenarios are listed in Table 7.2-5.

Table 7.2-5: Evaluation parameters for SMa scenarios

Parameters

SMa

Cell layout

Hexagonal grid, 19 macro sites, 3 sectors per site

(ISD = 1299 m and 1732 m, see note 1 and 2)

Up to two floors for residential buildings, up to five floors for commercial buildings. Building distribution are 90% residential and 10% commercial buildings

BS antenna height hBS

35 m

UT location

Outdoor/indoor

Outdoor and indoor

LOS/NLOS

LOS and NLOS

Height hUT

1.5 m for outdoor,

1.5 or 4.5 m for residential buildings,

1.5/4.5/7.5/10.5/13.5 m for commercial buildings

Indoor UT ratio

80%

UT mobility (horizontal plane only)

Indoor UTs: 3 km/h

Outdoor UTs (in car): 40 km/h

Min. BS - UT distance (2D)

35 m

UT distribution (horizontal)

Uniform

UT distribution (vertical)

Uniform distribution across all floors for a building type

N OTE 1:     For evaluation in study items/work items, if needed, admission control policies may be considered in conjunction with the ISD choice to address out of coverage UEs

NOTE 2:     SMa scenarios with ISDs between 1200-1800m can be used for evaluations.

 

7.3     Antenna modelling #

7.3.0     Antenna array structure #

This clause captures the antenna array structures considered in this SI for calibration.

BS antenna model:

The BS antenna is modelled by a uniform rectangular panel array, comprising MgNg panels, as illustrated in Figure 7.3-1 with Mg being the number of panels in a column and Ng being the number of panels in a row. Furthermore the following properties apply:

-    Antenna panels are uniformly spaced in the horizontal direction with a spacing of dg,H and in the vertical direction with a spacing of dg,V.

-    On each antenna panel, antenna elements are placed in the vertical and horizontal direction, where N is the number of columns, M is the number of antenna elements with the same polarization in each column.

-    Antenna numbering on the panel illustrated in Figure 7.3-1 assumes observation of the antenna array from the front (with x-axis pointing towards broad-side and increasing y-coordinate for increasing column number).

-    The antenna elements are uniformly spaced in the horizontal direction with a spacing of dH and in the vertical direction with a spacing of dV.

-    The antenna panel is either single polarized (P =1) or dual polarized (P =2).

The rectangular panel array antenna can be described by the following tuple Image.

Image

Figure 7.3-1: Cross-polarized panel array antenna model

The antenna radiation power pattern of each antenna element is generated according to Table 7.3-1.

Table 7.3-1: Radiation power pattern of a single antenna element

Parameter

Values

Vertical cut of the radiation power pattern (dB)

Image

Horizontal cut of the radiation power pattern (dB)

Image

3D radiation power pattern (dB)

Image

Maximum directional gain of an antenna element GE,max

8 dBi

 

UT antenna model:

The UT antenna can be modelled by placing the antenna element/array at the placement candidate locations relative to the centre of the UT. The size of the device can be set to (X cm, Y cm, Z cm), in depth, width, and height respectively.

For handheld UT devices, the following additional properties apply:

-    Four corners of the rectangle and the center of edges of the rectangle that reflect UT form factor are identified as potential locations of the UT antenna and potential locations are enumerated as illustrated in Figure 7.3-2.

-    Each antenna is assumed to be oriented along the direction determined by the vector connecting the centre of the rectangle to the antenna location.

-    Reference UT orientation vector of the handheld UT is perpendicular to the plane of the flat UT handheld device, and reference point for near field phase calculation of the UT is assumed to be the center of the plane of the UT handheld.

-    Device dimensions for UT antenna modelling are (15 cm, 7 cm, 0 cm).

Image

Figure 7.3-2: Handheld UT antenna placement candidate locations relative to centre of device (top down view)

-    Reference handheld UT orientation is illustrated in Figure 7.3-3. The representation the handheld UT orientation will be same between GCS and UT LCS for ΩUT,α = 0 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 0 degrees,

Image

Figure 7.3-3: Illustration of reference handheld UT orientation in UT LCS

-    For calibration of handheld UT, the following UT rotation based on reference UT orientation is assumed.

-    For calibration with spatial non-stationarity modelling:

-    For one-hand blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 45 degrees, ΩUT,γ = 0 degrees,

-    For dual-hand blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 45 degrees,

-    For hand and head blockage, ΩUT,α = 0 – 360 degrees, ΩUT,β = 90 degrees, ΩUT,γ = 0 degrees,

-    For all other cases:

-    ΩUT,α = 0 – 360 degrees, ΩUT,β = 45 degrees, ΩUT,γ = 0 degrees,

-    Example of UT rotations are illustrated in Figure 7.3-4 and 7.3-5.

Image

Figure 7.3-4: Example of UT rotation of ΩUT,α = 0 - 360 degrees, ΩUT,β = 90 degrees, ΩUT,γ = 0 degrees

Image

Figure 7.3-5: Example of UT rotation of ΩUT,α = 0 - 360 degrees, ΩUT,β = 0 degrees, ΩUT,γ = 90 degrees

-    The antenna radiation power pattern of each antenna element for handheld UT is generated according to Table 7.3-2, which assumes antenna is oriented in θ=90°and ϕ=0° direction.

Table 7.3-2: Radiation power pattern of a single antenna element for handheld UT

Parameter

Values

Vertical cut of the radiation power pattern (dB)

AdBθ, ϕ=0°=-min12θ-90°θ3dB2,SLAVwith θ3dB=125°,SLAV=22.5dB and θ, 180°

Horizontal cut of the radiation power pattern (dB)

AdBθ=90°, ϕ=-min12ϕϕ3dB2,Amaxwith ϕ3dB=125°, Amax=22.5 dB and ϕ-180°, 180°

3D radiation power pattern (dB)

AdBθ,ϕ=-min-AdBθ, ϕ=0°+AdBθ=90°, ϕ,Amax

Maximum directional gain of an antenna element GE,max

5.3 dBi

NOTE:    For UT antenna modelling of handheld devices, optional antenna imbalance can be modelled. If modelled, randomized loss is applied per UT antenna port and randomized loss can be applied independently for the UL and DL directions. No imbalance is modelled by default.

 

For consumer premise equipment (CPE) devices, the following properties apply:

-    Total 9 candidate antenna locations in 4 corners of the vertical square plane, 4 center of the edges of the vertical square plane, and center of the vertical square plane. The figure below captures the side view on the device. Note that a candidate antenna location (e.g., 9) can be used for multiple antennas.

-    Device dimensions for UT antenna modelling are (0 cm, 20 cm, 20 cm)

Image

Figure 7.3-6: CPE UT antenna placement candidate locations relative to centre of device (side view)

 

7.3.1     Antenna port mapping #

Legacy BS array antennas, i.e. uniform linear arrays with fix phase shifts between its M elements to obtain a beamtilt in vertical direction are modelled using complex weights

    wm=1Mexp-jλm-1dVcosθetilt    (7.3-1)

where m=1, …, M, θetilt is the electrical vertical steering angle defined between 0° and 180° (90° represents perpendicular to the array). λ denotes the wavelength and dV the vertical element spacing.

7.3.2     Polarized antenna modelling #

In general the relationship between radiation field and power pattern is given by:

    A(θ,ϕ'')=Fθ''''θ'',ϕ''2+Fϕ''''θ'',ϕ''2.    (7.3-2)

The following two models represent two options on how to determine the radiation field patterns based on a defined radiation power pattern.

Model-1:

In case of polarized antenna elements assume ζ is the polarization slant angle where ζ=0 degrees corresponds to a purely vertically polarized antenna element and ζ=±45 degrees correspond to a pair of cross-polarized antenna elements. Then the antenna element field components in θ' and ϕ' direction are given by

    Fθ''θ',ϕ'Fϕ''θ',ϕ'=+cosψ-sinψ+sinψ+cosψFθ''''θ'',ϕ''Fϕ''''θ'',ϕ'',    (7.3-3)

 

where

    cosψ=cosζsinθ'+sinζsinϕ'cosθ'1-cosζcosθ'-sinζsinϕ'sinθ'2,    (7.3-3a)

    sinψ=sinζcosϕ'1-cosζcosθ'-sinζsinϕ'sinθ'2.    (7.3-3b)

Note that the zenith and the azimuth field components Fθ''θ',ϕ', Fϕ''θ',ϕ', Fθ''''θ'',ϕ'' and Fϕ''''θ'',ϕ'' are defined in terms of the spherical basis vectors of an LCS as defined in Clause 7.1. The difference between the single-primed and the double-primed components is that the single-primed field components account for the polarization slant and the double-primed field components do not. For a single polarized antenna (purely vertically polarized antenna) we can write Fθ''''θ'',ϕ''=A(θ,ϕ''), and Fϕ''''θ'',ϕ''=0 where A(θ,ϕ''), is the 3D antenna radiation power pattern as a function of azimuth angle ϕ and zenith angle θ in the LCS as defined in Table 7.3-1 converted into linear scale.

Model-2:

In case of polarized antennas, the polarization is modelled as angle-independent in both azimuth and elevation, in an LCS. For a linearly polarized antenna, the antenna element field pattern, in the vertical polarization and in the horizontal polarization, are given by

    Fθ''θ',ϕ'=A'θ',ϕ'cosζ    (7.3-4)

and

    Fϕ''θ',ϕ'=A'θ',ϕ'sinζ,    (7.3-5)

respectively, where ζ is the polarization slant angle and A'θ',ϕ' is the 3D antenna element power pattern as a function of azimuth angle, ϕ' and elevation angle, θ' in the LCS. Note that ζ=0 degrees correspond to a purely vertically polarized antenna element. The vertical and horizontal field directions are defined in terms of the spherical basis vectors, θ' and ϕ' respectively in the LCS as defined in Clause 7.1.2. Also A'θ',ϕ'=A(θ,ϕ''), θ'=θand ϕ'=ϕ'' as defined in Table 7.1-1.

Handheld UT Model:

For cases when a candidate antenna placement location is used for one antenna field pattern:

-    Reference radiation pattern of the UT antenna model is vertically polarized with all the gain in the theta field component, Fθθ,ϕ=Aθ,ϕ and Fϕθ,ϕ=0, and referred to as the polarization direction along the Z'' axis.

For cases when a candidate antenna placement location is used for two distinct antenna polarization field patterns:

-    Reference radiation pattern of the UT antenna model is,

-    For first antenna field pattern: Fθθ,ϕ=Aθ,ϕ and Fϕθ,ϕ=0.

-    For second antenna field pattern: Fθθ,ϕ=0 and Fϕθ,ϕ=Aθ,ϕ.

Each polarized field component of the reference radiation pattern Fθθ,ϕ and Fϕθ,ϕ should be rotated according to the orientation and polarization direction of the each of UT antennae to get Fu,θ''θ',ϕ', Fu, ϕ''θ',ϕ' using equation

    Fu,θ''θ',ϕ'Fu, ϕ''θ',ϕ'=+cosψu-sinψu+sinψu+cosψuFθ''''θ'',ϕ''Fϕ''''θ'',ϕ'',    (7.3-6)

    cosψu=cosβucosγusinθ'-sinβucosγucosϕ'-αu-sinγusinϕ'-αucosθ'1-cosβucosγucosθ'+sinβucosγucosϕ'-αu-sinγusinϕ'-αusinθ'2,    (7.3-7)

    sinψu=sinβucosγusinϕ'-αu+sinγucosϕ'-αu1-cosβucosγucosθ'+sinβucosγucosϕ'-αu-sinγusinϕ'-αusinθ'2,    (7.3-8)

where the 3D-rotation angles, αu, βu and γu, are obtained according to the orientation and polarization direction of each UT antenna u, the zenith and azimuth angles mapping between θ',ϕ' and θ'',ϕ'' are based on equations (7.1-7) and (7.1-8) using the same 3D-rotation angles, αu, βu and γu, and rotated based on the orientation of the UT in the global coordinate system to get Fu,θθ,ϕ and Fu,ϕθ,ϕ using Clause 7.1.3 equation (7.1-11).

For cases when a candidate antenna placement location is used for one antenna field pattern (e.g., single polarization):

-    The polarization direction is indicated by the arrow in Figure 7.3-7, which is parallel with the plane of the handheld UT and perpendicular to the direction from the UT center to the candidate antenna location.

Image

Figure 7.3-7: Handheld UT antenna polarization directions for one antenna field pattern (top down view)

For cases when a candidate antenna placement location is used for two antenna field patterns (e.g., dual polarization) (not intended for FR1):

-    For the first antenna field pattern, the polarization direction is indicated by the arrows in Figure 7.3-7 but additionally rotated 45 degrees about the direction from the UT center to the candidate antenna location (i.e. rotated using the direction from the UT center to the candidate antenna location as the rotational axis).

-    For the second antenna field pattern, the polarization direction is perpendicular to the polarization direction of the first filed pattern and perpendicular to the direction from the UT center to the candidate antenna location

-    An example for candidate antenna location (6) is given in Figure 7.3-8.

Image

Figure 7.3-8: Handheld UT polarization direction for two antenna filed pattern (side view)

 

7.4     Pathloss, LOS probability and penetration modelling #

7.4 .1    Pathloss #

The pathloss models are summarized in Table 7.4.1-1 and the distance definitions are indicated in Figure 7.4.1-1 and Figure 7.4.1-2. Note that the distribution of the shadow fading is log-normal, and its standard deviation for each scenario is given in Table 7.4.1-1.

 

Image

Image

Figure7.4.1-1: Definition of d2D and d3D for outdoor UTs<br>

Figure 7.4.1-2: Definition of d2D-out, d2D-in and <br>d3D-out, d3D-in for indoor UTs.

Note that

    Image    (7.4-1)

 

Table 7.4.1-1: Pathloss models

Scenario

LOS/NLOS

Pathloss [dB], fc is in GHz and d is in meters, see note 6

Shadow

fading

std [dB]

Applicability range,

antenna height

default values

RMa

LOS

PLRMa-LOS=PL110m≤d2DdBPPL2dBPd2D≤10km, see note 5

 

PL1=20log10(40πd3Dfc/3)+min(0.03h1.72,10)log10(d3D) -min(0.044h1.72,14.77)+0.002log10(h)d3D

PL2=PL1(dBP)+40log10(d3D/dBP)

 

 

For 10 m ≤ d2D < dBP,

σSF=4

 

For dBP ≤ d2D < 10 km,

σSF=6

 

hBS=35m

hUT=1.5m

W=20m

h=5m

h = avg. building height

W = avg. street width

The applicability ranges:

5mh50m

5mW50m

10mhBS150m

1mhUT10m

NLOS

PLRMa-NLOS=max(PLRMa-LOS,PLRMa-NLOS'), for 10m≤d2D≤5km

 

PLRMa-NLOS'=161.04-7.1log10(W)+7.5log10(h) -(24.37-3.7(h/hBS)2)log10(hBS) +(43.42-3.1log10(hBS))(log10(d3D)-3) +20log10(fc)-(3.2(log10(11.75hUT))2-4.97)

 

 

σSF=8

UMa

LOS

PLUMa-LOS=PL110m≤d2DdBP'PL2dBP'd2D≤5km, see note 1

 

PL1=28.0+22log10(d3D)+20log10(fc)

PL2=28.0+40log10(d3D)+20log10(fc) -9log10((dBP')2+(hBS-hUT)2)

 

 

 

 

σSF=4

1.5mhUT22.5m

hBS=25m

See note 7

NLOS

PLUMa-NLOS=max(PLUMa-LOS,PLUMa-NLOS'), for 10m≤d2D≤5km

 

PLUMa-NLOS'=13.54+39.08log10(d3D)+ 20log10(fc)-0.6(hUT-1.5)

 

 

σSF=6

1.5mhUT22.5m

hBS=25m

Explanations: see note 3

Optional PLUMa-NLOS=32.4+20log10fc+30log10d3D

σSF=7.8

 

UMi - Street Canyon

LOS

PLUMi-LOS=PL110m≤d2DdBP'PL2dBP'd2D≤5km, see note 1

 

PL1=32.4+21log10(d3D)+20log10(fc)

PL2=32.4+40log10(d3D)+20log10(fc) -9.5log10((dBP')2+(hBS-hUT)2)

σSF=4

1.5mhUT22.5m

hBS=10m

See note 7

NLOS

PLUMi-NLOS=max(PLUMi-LOS,PLUMi-NLOS'), for 10m≤d2D≤5km

 

PLUMi-NLOS'=35.3log10(d3D)+22.4 + 21.3log10(fc)-0.3(hUT-1.5)

σSF=7.82

1.5mhUT22.5m

hBS=10m

Explanations: see note 4

Optional PLUMi-NLOS=32.4+20log10fc+31.9log10d3D

σSF=8.2

 

InH - Office

LOS

PLInH-LOS=32.4+17.3log10(d3D)+20log10(fc)

σSF=3

1md3D150m

NLOS

PLInH-NLOS=max(PLInH-LOS,PLInH-NLOS')

PLInH-NLOS'=38.3log10d3D+17.30+24.9log10fc

σSF=8.03

1md3D150m

Optional PLInH-NLOS'=32.4+20log10fc+31.9log10d3D

σSF=8.29

1md3D150m

InF

LOS

PLLOS=31.84+21.50log10d3D+19.00log10fc

σSF=4.3

1≤d3D≤600 m

NLOS

InF-SL: PL=33+25.5log10d3D+20log10fc

PLNLOS=maxPL,PLLOS

σSF=5.7

InF-DL: PL=18.6+35.7log10d3D+20log10fc

PLNLOS=maxPL,PLLOS,PLInF-SL

σSF=7.2

InF-SH: PL=32.4+23.0log10d3D+20log10fc

PLNLOS=maxPL,PLLOS

σSF=5.9

InF-DH: PL=33.63+21.9log10d3D+20log10fc

PLNLOS=maxPL,PLLOS

σSF=4.0

SMa

LOS

PLSMa-LOS=PL110m≤d2D<dBPPL2dBPd2D≤5km,

 

PL1=20log1040πd3Dfc3+min0.03h1.72,10log10d3D- min0.044h1.72, 14.77+0.002log10hd3D, see note 5

PL2=PL1dBP+40log10d3DdBP, see note 5

For 10 m ≤ d2D < dBP,

σSF=4

For dBP ≤ d2D < 5000 m,

σSF=6

10 m d2D < dBP

dBP d2D < 5000 m

25 m < hBS < 35 m

h = 10 m

NLOS

PLSMa-NLOS=161.04-7.1log10W+7.5log10h-24.37-3.7hhBS2log10hBS+43.42-3.1log10hBSlog10d3D-3+20log10fc-3.2log1011.75hUT2-4.97

σSF=8

10 m d2D < 5000 m

1 m < hUT < 14 m

h = 10 m, W = 10 m

NOTE 1:     Effective breakpoint distance d'BP = 4 h'BS h'UT fc/c, where fc is the centre frequency in Hz, c = 3.0108 m/s is the propagation velocity in free space, and h'BS and h'UT are the effective antenna heights at the BS and the UT, respectively. The effective antenna heights h'BS and h'UT are computed as follows: h'BS = hBShE, h'UT = hUThE, where hBS and hUT are the actual antenna heights, and hE is the effective environment height. For UMi hE = 1.0m. For UMa hE=1m with a probability equal to 1/(1+C(d2D, hUT)) and chosen from a discrete uniform distribution uniform(12,15,…,(hUT-1.5)) otherwise. With C(d2D, hUT) given by

    Cd2D,hUT=0,hUT<13mhUT-13101.5gd2D,13mhUT23m,

    where

    gd2D=0,d2D18m54d2D1003exp-d2D150,18m<d2D.

    Note that hE depends on d2D and hUT and thus needs to be independently determined for every link between BS sites and UTs. A BS site may be a single BS or multiple co-located BSs.

NOTE 2:     The applicable frequency range of the PL formula in this table is 0.5 < fc < fH GHz, where fH = 30 GHz for RMa, fH = 37 GHz for SMa, and fH = 100 GHz for all the other scenarios. It is noted that RMa pathloss model for >7 GHz is validated based on a single measurement campaign conducted at 24 GHz.

NOTE 3:     UMa NLOS pathloss is from TR36.873 with simplified format and PLUMa-LOS = Pathloss of UMa LOS outdoor scenario.

NOTE 4:     PLUMi-LOS = Pathloss of UMi-Street Canyon LOS outdoor scenario.

NOTE 5:     Break point distance dBP = 2π hBS hUT fc/c, where fc is the centre frequency in Hz, c = 3.0 108 m/s is the propagation velocity in free space, and hBS and hUT are the antenna heights at the BS and the UT, respectively.

NOTE 6:     fc denotes the center frequency normalized by 1GHz, all distance related values are normalized by 1m, unless it is stated otherwise.

NOTE 7:     UMi and UMa pathloss formula converges for different frequencies when distances beyond the breakpoint distance is applied.

 

7.4 .2    LOS probability #

The Line-Of-Sight (LOS) probabilities are given in Table 7.4.2-1.

Table 7.4.2-1 LOS probability

Scenario

LOS probability (distance is in meters)

RMa

Image

UMi - Street canyon

Image

UMa

Imagewhere

Image

 

Indoor - Mixed office

Image

Indoor - Open office

Image

InF-SL

InF-SH

InF-DL

InF-DH

PrLOS,subsced2D=exp-d2Dksubsce

where

ksubsce=-dclutterln1-rfor InF-SL and InF-DL-dclutterln1-rhBS-hUThc-hUTfor InF-SH and InF-DH

The parameters dclutter, r, and hc are defined in Table 7.2-4

InF-HH

PrLOS=1

SMa

PLOS=1, d2D-out≤10 mexp-d2D-outkcommercialexp-d2D-outkresidentialexp-d2D-outkvegetation, d2D-out>10 m

where

1kcommercial=-ln1-rcommericialdclutterhcommercial-hUThBS-hUT

1kresidential=-ln1-rresidentialdcluttermaxhresidential-hUT ,0hBS-hUT

1kvegetation=-ln1-rvegetationdclutterhvegetation-hUThBS-hUT

dclutter=30 m, hcommercial = 20 m, hresidential= 8 m, hvegetation = 15 m

rcommericial=2%, rresidential=18%, rvegetation=0% (no vegetation), 10% (sparse vegetation), or 20% (dense vegetation)

NOTE:    The LOS probability is derived with assuming antenna heights of 3m for indoor, 10m for UMi, and 25m for UMa

 

7.4 .3    O2I penetration loss #

7.4 .3.1    O2I building penetration loss #

The pathloss incorporating O2I building penetration loss is modelled as in the following:

    PL=PLb+PLtw+PLin+N0,σP2    (7.4-2)

where PLb is the basic outdoor path loss given in Clause 7.4.1, where d3D is replaced by d3D-out+d3D-in . PLtw is the building penetration loss through the external wall, PLin is the inside loss dependent on the depth into the building, and σP is the standard deviation for the penetration loss.

PLtw is characterized as:

    PLtw=PLnpi-10log10i=1Npi×10Lmateriali-10    (7.4-3)

PLnpi is an additional loss is added to the external wall loss to account for non-perpendicular incidence; Lmateriali=amateriali+bmaterialif, is the penetration loss of material i, example values of which can be found in Table 7.4.3-1; pi is proportion of i-th materials, where i=1Npi=1; and N is the number of materials.

Table 7.4.3-1: Material penetration losses

Material

Penetration loss [dB]

Notes on Material

Standard multi-pane glass

Lglass=2+0.2f

Reference thickness of 3 cm assumed

IRR glass

LIRRglass=25.4+0.11f, see note 2

Single coating is assumed

Concrete

Lconcrete=5+4f

Reference thickness of 23 cm assumed

Plywood

Lplywood=1.03+0.17f

Reference thickness of 1.75 cm assumed

Wood

Lwood=4.85+0.12f

Reference thickness of 3.3 cm assumed

NOTE 1:     f is in GHz

NOTE 2: Values marked with this note were updated as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR.

 

Table 7.4.3-2 gives PLtw, PLin and σP for two O2I penetration loss models. The O2I penetration is UT-specifically generated, and is added to the SF realization in the log domain.

Table 7.4.3-2: O2I building penetration loss model

 

Path loss through external wall:

PLtw in [dB]

Indoor loss:

PLin in [dB]

Standard deviation:

σP in [dB]

Low-loss model

5-10log100.310-Lglass10+0.710-Lconcrete10

0.5 d2D-in

4.4

High-loss model

5-10log100.710-LIRRglass10+0.310-Lconcrete10

0.5 d2D-in

6.5

Low-loss A model

5-10log100.3⋅10-Lglass10+0.7⋅10-Lplywood10

0.5 d2D-in

4.4

 

d2D-in is minimum of two independently generated uniformly distributed variables between 0 and 25 m for UMa and UMi-Street Canyon, between 0 and 25 m for SMa UTs in commercials buildings, between 0 and 10 m for SMa UTs in residential buildings, and between 0 and 10 m for RMa. d2D-in shall be UT-specifically generated.

Both low-loss and high-loss models are applicable to UMa and UMi-Street Canyon.

Only the low-loss model is applicable to RMa.

Only the high-loss model is applicable to InF.

Low-loss, high-loss, and low-loss A models are applicable to SMa.

The composition of low and high loss is a simulation parameter that should be determined by the user of the channel models, and is dependent on the use of metal-coated glass in buildings and the deployment scenarios. Such use is expected to differ in different markets and regions of the world and also may increase over years to new regulations and energy saving initiatives. Furthermore, the use of such high-loss glass currently appears to be more predominant in commercial buildings than in residential buildings in some regions of the world (see note).

Note:    One example survey for the US market can be found in [5]. The survey does not necessarily be representative for all the scenarios. Other ratios outside of the survey should not be precluded.

For backwards compatibility with TR 36.873 [3], the following building penetration model should be used for UMa and UMi single-frequency simulations at frequencies below 6 GHz.

Table 7.4.3-3. O2I building penetration loss model for single-frequency simulations <6 GHz

Parameter

Value

PLtw

20 dB

PLin

0.5 d2D-in

with d2D-in being a single, link-specific, uniformly distributed variable between 0 and 25 m

σP

0 dB

σSF

7 dB (note: replacing the respective value in Table 7.4.1-1)

 

7.4 .3.2    O2I car penetration loss #

The pathloss incorporating O2I car penetration loss is modelled as in the following:

    Image    (7.4-4)

where Image is the basic outdoor path loss given in Clause 7.4.1. μ = 9, and σP = 5. The car penetration loss shall be UT-specifically generated. Optionally, for metallized car windows, μ = 20 can be used. The O2I car penetration loss models are applicable for at least 0.6-60 GHz.

7.4.4     Autocorrelation of shadow fading #

The long-term (log-normal) fading in the logarithmic scale around the mean path loss PL (dB) is characterized by a Gaussian distribution with zero mean and standard deviation. Due to the slow fading process versus distance x (<br>x is in the horizontal plane), adjacent fading values are correlated. Its normalized autocorrelation function R(x) can be described with sufficient accuracy by the exponential function ITU-R Rec. P.1816 [18]

    Image    (7.4-5)

with the correlation length dcor being dependent on the environment, see the correlation parameters for shadowing and other large scale parameters in Table 7.5-6 (Channel model parameters). In a spatial consistency procedure in Clause 7.6.3, the cluster specific random variables are also correlated following the exponential function with respect to correlation distances in the two dimensional horizontal plane.

7.5     Fast fading model #

The radio channel realizations are created using the parameters listed in Table 7.5-1. The channel realizations are obtained by a step-wise procedure illustrated in Figure 7.5-1 and described below. It has to be noted that the geometric description covers arrival angles from the last bounce scatterers and respectively departure angles to the first scatterers interacted from the transmitting side. The propagation between the first and the last interaction is not defined. Thus, this approach can model also multiple interactions with the scattering media. This indicates also that e.g., the delay of a multipath component cannot be determined by the geometry. In the following steps, downlink is assumed. For uplink, arrival and departure parameters have to be swapped.

Note: the channel generation in this clause is enough for at least the following cases.

-    Case 1: For low complexity evaluations

-    Case 2: To compare with earlier simulation results,

-    Case 3: When none of the additional modelling components are turned on.

For other advanced simulations, e.g., spatially consistency, large bandwidth and arrays, oxygen absorption, blockage, absolute time of arrival, dual mobility, embedded devices, etc., some of the additional modelling components of Clause 7.6 should be considered.

 

Image

Figure 7.5-1 Channel coefficient generation procedure

 

ImageFigure 7.5-2: Definition of a global coordinate system showing the zenith angle θ and the azimuth angle ϕ. θ= points to zenith and θ=+90° points to the horizon. The spherical basis vectors <br>Image and Image shown above are defined based on the direction of propagationImage.

 

Table 7.5-1: Notations in the global coordinate system (GCS)

Parameter

Notation

LOS AOD

ϕLOS,AOD

LOS AOA

ϕLOS,AOA

LOS ZOD

θLOS,ZOD

LOS ZOA

θLOS,ZOA

AOA for cluster n

ϕn,AOA

AOD for cluster n

ϕn,AOD

AOA for ray m in cluster n

ϕn,m,AOA

AOD for ray m in cluster n

ϕn,m,AOD

ZOA for cluster n

θn,ZOA

ZOD for cluster n

θn,ZOD

ZOA for ray m in cluster n

θn,m,ZOA

ZOD for ray m in cluster n

θn,m,ZOD

Receive antenna element u field pattern in the direction of the spherical basis vector Image

Frx,u,θ

Receive antenna element u field pattern in the direction of the spherical basis vector Image

Frx,u,ϕ

Transmit antenna element s field pattern in the direction of the spherical basis vector Image

Ftx,s,θ

Transmit antenna element s field pattern in the direction of the spherical basis vector Image

Frx,s,ϕ

 

Step 1: Set environment, network layout, and antenna array parameters

a)    Choose one of the scenarios (e.g. UMa, UMi-Street Canyon, RMa, InH-Office or InF). Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors Image, Image as shown in Figure 7.5-2. Note: Scenario RMa is for up to 7GHz while others are for up to 100GHz

b)    Give number of BS and UT

c)    Give 3D locations of BS and UT, and determine LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), and LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system

d)    Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries

e)    Give BS and UT orientations with respect to the global coordinate system. BS array orientation is defined by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant angle).

f)    Give speed and direction of motion of UT in the global coordinate system

g)    Specify system centre frequency Image and bandwidth Image

Note:    In case wrapping is used, each wrapping copy of a BS or site should be treated as a separate BS/site considering channel generation.

 

Large scale parameters:

Step 2: Assign propagation condition (LOS/NLOS) according to Table 7.4.2-1. The propagation conditions for different BS-UT links are uncorrelated.

Also, assign an indoor/outdoor state for each UT. It is noted that all the links from a UT have the same indoor/outdoor state.

Step 3: Calculate pathloss with formulas in Table 7.4.1-1 for each BS-UT link to be modelled.

Step 4: Generate large scale parameters, e.g. delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K factor (K) and shadow fading (SF) taking into account cross correlation according to Table 7.5-6 and using the procedure described in clause 3.3.1 of [14] with the square root matrixImagebeing generated using the Cholesky decomposition and the following order of the large scale parameter vector: sM = [sSF, sK, sDS, sASD, sASA, sZSD, sZSA]T.

These LSPs for different BS-UT links are uncorrelated, but the LSPs for links from co-sited sectors to a UT are the same. In addition, these LSPs for the links of UTs on different floors are uncorrelated.

Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA, 104), ASD = min(ASD, 104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA, 52), ZSD = min(ZSD, 52).

 

Small scale parameters:

Step 5: Generate cluster delays Image

Delays are drawn randomly from the delay distribution defined in Table 7.5-6. With exponential delay distribution calculate

    Image,    (7.5-1)

Where rτ is the delay distribution proportionality factor, Xn ~ uniform(0,1), and cluster index n = 1,…,N. With uniform delay distribution the delay values Image are drawn from the corresponding range. Normalise the delays by subtracting the minimum delay and sort the normalised delays to ascending order:

    Image.    (7.5-2)

In the case of LOS condition, additional scaling of delays is required to compensate for the effect of LOS peak addition to the delay spread. The heuristically determined Ricean K-factor dependent scaling constant is

    Image,    (7.5-3)

where K [dB] is the Ricean K-factor as generated in Step 4. The scaled delays

    Image,    (7.5-4)

are not to be used in cluster power generation.

 

Step 6: Generate cluster powers Image.

Cluster powers are calculated assuming a single slope exponential power delay profile. Power assignment depends on the delay distribution defined in Table 7.5-6. With exponential delay distribution the cluster powers are determined by

    Image    (7.5-5)

where Image is the per cluster shadowing term in [dB]. Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,

    Image    (7.5-6)

In the case of LOS condition an additional specular component is added to the first cluster. Power of the single LOS ray is:

    Image    (7.5-7)

and the cluster powers are not normalized as in equation (7.5-6) , but:

    Image    (7.5-8)

where δ(.) is Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale. These power values are used only in equations (7.5-9) and (7.5-14), but not in equation (7.5-22).

Assign the power of each ray within a cluster as P/ M, where M is the number of rays per cluster.

Remove clusters with less than -25 dB power compared to the maximum cluster power based on equation (7.5-6). The scaling factors need not be changed after cluster elimination.

 

Step 7: Generate arrival angles and departure angles for both azimuth and elevation.

The composite PAS in azimuth of all clusters is modelled as wrapped Gaussian. The AOAs are determined by applying the inverse Gaussian function (7.5-9) with input parameters Pn and RMS angle spread ASA

    Image,    (7.5-9)

with Image defined as

    Image,    (7.5-10)

where Image is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2:

Table 7.5-2: Scaling factors for AOA, AOD generation

# clusters

4

5

6

7

8

 

10

11

12

CφNLOS

0.779

0.860

0.921

0.973

1.018

 

1.090

1.123

1.146

# clusters

 

14

15

16

 

 

19

20

25

CφNLOS

 

1.190

1.211

1.226

 

 

1.273

1.289

1.358

 

In the LOS case, constant Image also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.

 

Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component Image to introduce random variation

    Image,    (7.5-11)

where ϕLOS,AOA is the LOS direction defined in the network layout description, see Step1c.

In the LOS case, substitute (7.5-11) by (7.5-12) to enforce the first cluster to the LOS direction ϕLOS, AOA

    Image    (7.5-12)

Finally add offset angles αm from Table 7.5-3 to the cluster angles

    Image,    (7.5-13)

where cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6.

Table 7.5-3: Ray offset angles within a cluster, given for rms angle spread normalized to 1

Ray number m

Basis vector of offset angles αm

1,2

± 0.0447

3,4

± 0.1413

5,6

± 0.2492

7,8

± 0.3715

9,10

± 0.5129

11,12

± 0.6797

13,14

± 0.8844

15,16

± 1.1481

17,18

± 1.5195

19,20

± 2.1551

 

The generation of AOD (Image) follows a procedure similar to AOA as described above.

The generation of ZOA assumes that the composite PAS in the zenith dimension of all clusters is Laplacian (see Table 7.5-6). The ZOAs are determined by applying the inverse Laplacian function (7.5-14) with input parameters Pn and RMS angle spread ZSA

    Image,    (7.5-14)

with Image defined as

    Image,    (7.5-15)

Where Image is a scaling factor related to the total number of clusters and is given in Table 7.5-4:

Table 7.5-4: Scaling factors for ZOA, ZOD generation

# clusters

 

 

6

7

8

 

10

11

12

CθNLOS

 

 

0.788

0.847

0.889

 

0.957

1.031

1.104

# clusters

 

14

15

16

 

 

19

20

25

CθNLOS

 

1.1072

1.1088

1.1276

 

 

1.184

1.178

1.282

 

In the LOS case, constant Image also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.

Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component Image to introduce random variation

    Image,    (7.5-16)

where Image if the BS-UT link is O2I and Imageotherwise. The LOS direction is defined in the network layout description, see Step1c.

In the LOS case, substitute (7.5-16) by (7.5-17) to enforce the first cluster to the LOS direction θLOS,ZOA

    Image.    (7.5-17)

Finally add offset angles αm from Table 7.5-3 to the cluster angles

    Image,    (7.5-18)

where cZSA is the cluster-wise rms spread of ZOA (cluster ZSA) in Table 7.5-6. Assuming that Imageis wrapped within [0, 360°], if Image, thenImageis set to Image.

The generation of ZOD follows the same procedure as ZOA described above except equation (7.5-16) is replaced by

    Image,    (7.5-19)

where variable Xn is with uniform distribution to the discrete set of {1,–1}, Image, Imageis given in Tables 7.5-6/7/8 and equation (7.5-18) is replaced by

    Image    (7.5-20)

where Imageis the mean of the ZSD log-normal distribution.

In the LOS case, the generation of ZOD follows the same procedure as ZOA described above using equation (7.5-17).

 

Step 8: Coupling of rays within a cluster for both azimuth and elevation

Couple randomly AOD angles φn,m,AOD to AOA angles φn,m,AOA within a cluster n, or within a sub-cluster in the case of two strongest clusters (see Step 11 and Table 7.5-3). Couple randomly ZOD angles Image with ZOA angles Imageusing the same procedure. Couple randomly AOD angles φn,m,AOD with ZOD angles Imagewithin a cluster n or within a sub-cluster in the case of two strongest clusters.

 

Step 9: Generate the cross polarization power ratios

Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n. XPR is log-Normal distributed. Draw XPR values as

    Image,    (7.5-21)

where Image is Gaussian distributed with Imageand Image from Table 7.5-6.

Note: Image is independently drawn for each ray and each cluster.

The outcome of Steps 1-9 shall be identical for all the links from co-sited sectors to a UT.

 

Coefficient generation:

Step 10: Draw initial random phases

Draw random initial phase Image for each ray m of each cluster n and for four different polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-ππ).

 

Step 11: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.

The method described below is used at least for drop-based evaluations irrespective of UT speeds. Relevant cases for drop-based evaluations are:

-    Case 1: For low complexity evaluations

-    Case 2: To compare with earlier simulation results,

-    Case 3: When none of the additional modelling components are turned on.

-    Case 4: When spatial consistency and/or blockage is modeled for MU-MIMO simulations

-    Other cases are not precluded

For the N – 2 weakest clusters, say n = 3, 4,…, N, the channel coefficients are given by:

    Image    (7.5-22)

where Frx,u,θ and Frx,u,ϕ are the field patterns of receive antenna element u according to (7.1-11) and in the direction of the spherical basis vectors, Image and Image respectively, Ftx,s,θ and Ftx,s,ϕ are the field patterns of transmit antenna element s in the direction of the spherical basis vectors, Image and Image respectively. Note that the patterns are given in the GCS and therefore include transformations with respect to antenna orientation as described in Clause 7.1. Image is the spherical unit vector with azimuth arrival angle ϕn,m,AOA and elevation arrival angle θn,m,ZOA, given by

    Image,    (7.5-23)

where n denotes a cluster and m denotes a ray within cluster n. Image is the spherical unit vector with azimuth departure angle ϕn,m,AOD and elevation departure angle θn,m,ZOD, given by

    Image,    (7.5-24)

where n denotes a cluster and m denotes a ray within cluster n. Also, Imageis the location vector of receive antenna element u and Imageis the location vector of transmit antenna element s, κn,m is the cross polarisation power ratio in linear scale, and λ0 is the wavelength of the carrier frequency. If polarisation is not considered, the 2x2 polarisation matrix can be replaced by the scalar Image and only vertically polarised field patterns are applied.

The Doppler frequency component depends on the arrival angles (AOA, ZOA), and the UT velocity vector Imagewith speed v, travel azimuth angle ϕv, elevation angle θv and is given by

    Image    (7.5-25)

For the two strongest clusters, say n = 1 and 2, rays are spread in delay to three sub-clusters (per cluster), with fixed delay offset. The delays of the sub-clusters are

    Image    (7.5-26)

where Image is cluster delay spread specified in Table 7.5-6. When intra-cluster delay spread is unspecified (i.e., N/A) the value 3.91 ns is used; it is noted that this value results in the legacy behaviour with 5 and 10 ns sub-cluster delays.

Twenty rays of a cluster are mapped to sub-clusters as presented in Table 7.5-5 below. The corresponding offset angles are taken from Table 7.5-3 with mapping of Table 7.5-5.

Table 7.5-5: Sub-cluster information for intra cluster delay spread clusters

sub-cluster #

Image

mapping to rays

Image

Power

Image

delay offset

Image

Image

Image

10/20

0

Image

Image

6/20

1.28 Image

Image

Image

4/20

2.56 Image

 

Then, the channel impulse response is given by:

    Image    (7.5-27)

where Image is given in (7.5-22) and Image defined as:

    Image    (7.5-28)

In the LOS case, determine the LOS channel coefficient by:

    Image    (7.5-29)

where δ(.) is the Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale.

Then, the channel impulse response is given by adding the LOS channel coefficient to the NLOS channel impulse response and scaling both terms according to the desired K-factor Image as

    Image.    (7.5-30)

 

Step 12: Apply pathloss and shadowing for the channel coefficients.

 

 

 

Table 7.5-6 Part-1: Channel model parameters for UMi-Street Canyon and UMa

Scenarios

UMi - Street Canyon

UMa

LOS

NLOS

O2I

LOS

NLOS

O2I

Delay spread (DS)

lgDS=log10(DS/1s)

μlgDS

-0.18 log10(1+ fc) - 7.28,

see note 8

-0.22 log10(1+ fc) - 6.87,

see note 8

-6.62

-7.067 - 0.0794 log10(fc),

see note 8

-6.47 - 0.134 log10(fc),

see note 8

-6.62

σlgDS

0.39,

see note 8

0.19 log10(1+ fc) + 0.22,

see note 8

0.32

0.57 + 0.026 log10(fc),

see note 8

0.39

see note 9

0.32

AOD spread (ASD)

lgASD=log10(ASD/1)

μlgASD

-0.05 log10(1+ fc) + 1.21

see note 9

-0.24 log10(1+ fc) + 1.54

see note 8

1.25

0.92,

see note 8

1.09,

see note 8

0.58,

see note 8

σlgASD

0.08 log10(1+ fc) + 0.29

see note 8

0.10 log10(1+ fc) + 0.33

see note 8

0.42

0.31,

see note 8

0.44,

see note 8

0.7

see note 8

AOA spread (ASA)

lgASA=log10(ASA/1)

μlgASA

-0.07 log10(1+ fc) + 1.66,

see note 8

-0.07 log10(1+ fc) + 1.76,

see note 8

1.76

1.76,

see note 8

2.04 - 0.25 log10(fc),

see note 8

1.76

σlgASA

0.021 log10(1+ fc) + 0.26,

see note 8

0.05 log10(1+ fc) + 0.27,

see note 8

0.16

0.19,

see note 8

0.17 - 0.03 log10(fc),

see note 8

0.16

ZOA spread (ZSA)

lgZSA=log10(ZSA/1)

μlgZSA

-0.11 log10(1+ fc) + 0.81

see note 8

-0.03 log10(1+ fc) + 0.92

see note 8

1.01

0.96

see note 8

-0.2856 log10(fc) + 1.445,

see note 8

1.01

σlgZSA

-0.03 log10(1+ fc) + 0.29

see note 8

-0.05 log10(1+ fc) + 0.35

see note 8

0.43

0.15

see note 8

0.17,

see note 8

0.43

Shadow fading (SF) [dB]

σSF

See Table 7.4.1-1

See Table 7.4.1-1

7

See Table 7.4.1-1

See Table 7.4.1-1

7

K-factor (K) [dB]

μK

9

N/A

N/A

9

N/A

N/A

σK

5

N/A

N/A

3.5

N/A

N/A

Cross-Correlations

ASD vs DS

0.5

0

0.4

0.4

0.4

0.4

ASA vs DS

0.8

0.4

0.4

0.8

0.6

0.4

ASA vs SF

-0.4

-0.4

0

-0.5

0

0

ASD vs SF

-0.5

0

0.2

-0.5

-0.6

0.2

DS vs SF

-0.4

-0.7

-0.5

-0.4

-0.4

-0.5

ASD vs ASA

0.4

0

0

0

0.4

0

ASD vs

-0.2

N/A

N/A

0

N/A

N/A

ASA vs

-0.3

N/A

N/A

-0.2

N/A

N/A

DS vs

-0.7

N/A

N/A

-0.4

N/A

N/A

SF vs

0.5

N/A

N/A

0

N/A

N/A

Cross-Correlations 1)

ZSD vs SF

0

0

0

0

0

0

ZSA vs SF

0

0

0

-0.8

-0.4

0

ZSD vs K

0

N/A

N/A

0

N/A

N/A

ZSA vs K

0

N/A

N/A

0

N/A

N/A

ZSD vs DS

0

-0.5

-0.6

-0.2

-0.5

-0.6

ZSA vs DS

0.2

0

-0.2

0

0

-0.2

ZSD vs ASD

0.5

0.5

-0.2

0.5

0.5

-0.2

ZSA vs ASD

0.3

0.5

0

0

-0.1

0

ZSD vs ASA

0

0

0

-0.3

0

0

ZSA vs ASA

0

0.2

0.5

0.4

0

0.5

ZSD vs ZSA

0

0

0.5

0

0

0.5

Delay scaling parameter rτ

3

2.1

2.2

2.5

2.3

2.2

XPR [dB]

μXPR

9

8.0

9

8

7

9

σXPR

3

3

5

4

3

5

Number of clusters Image

12

19

12

12

20

12

Number of rays per cluster Image

20

20

20

20

20

20

Cluster DS (Image) in [ns]

5

11

11

max(0.25, 6.5622

-3.4084 log10(fc))

max(0.25, 6.5622

-3.4084 log10(fc))

11

Cluster ASD (Image) in [deg]

3

10

5

3.58,

see note 8

1.8,

see note 8

1.8,

see note 8

Cluster ASA (Image) in [deg]

17

22

8

11

15

8

Cluster ZSA (Image) in [deg]

7

7

3

7

7

3

Per cluster shadowing std ζ [dB]

3

3

4

3

3

4

Correlation distance in the horizontal plane [m]

DS

7

10

10

30

40

10

ASD

8

10

11

18

50

11

ASA

8

9

17

15

50

17

SF

10

13

7

37

50

7

15

N/A

N/A

12

N/A

N/A

ZSA

12

10

25

15

50

25

ZSD

12

10

25

15

50

25

f c is carrier frequency in GHz; d2D is BS-UT distance in km.

NOTE 1:     DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.

NOTE 2:     The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.

NOTE 3:    All large scale parameters are assumed to have no correlation between different floors.

NOTE 4:    The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.

NOTE 5:     For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution.

NOTE 6:     For UMa and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values

NOTE 7:     For UMi and frequencies below 2 GHz, use fc = 2 when determining the values of the frequency-dependent LSP values

NOTE 8:     Value(s) marked with this note were updated as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR [25].

NOTE 9:     Value(s) were unchanged even after the considering measurements as part of Release 19 Study on channel modelling enhancements for 7-24 GHz for NR [25].

 

Table 7.5-6 Part-2: Channel model parameters for RMa (up to 7GHz) and Indoor-Office

Scenarios

RMa

Indoor-Office

LOS

NLOS

O2I

LOS

NLOS

Delay spread (DS)

lgDS=log10(DS/1s)

μlgDS

-7.49

-7.43

-7.47

-0.01 log10(1+fc) - 7.692

-0.28 log10(1+fc) - 7.173

σlgDS

0.55

0.48

0.24

0.18

0.10 log10(1+fc) + 0.055

AOD spread (ASD)

lgASD=log10(ASD/1)

μlgASD

0.90

0.95

0.67

1.60

1.62

σlgASD

0.38

0.45

0.18

0.18

0.25

AOA spread (ASA)

lgASA=log10(ASA/1)

μlgASA

1.52

1.52

1.66

-0.19 log10(1+fc) + 1.781

-0.11 log10(1+fc) + 1.863

σlgASA

0.24

0.13

0.21

0.12 log10(1+fc) + 0.119

0.12 log10(1+fc) + 0.059

ZOA spread (ZSA)

lgZSA=log10(ZSA/1)

μlgZSA

0.47

0.58

0.93

-0.26 log10(1+fc) + 1.44

-0.15 log10(1+fc) + 1.387

σlgZSA

0.40

0.37

0.22

-0.04 log10(1+fc) + 0.264

-0.09 log10(1+fc) + 0.746

Shadow fading (SF) [dB]

σSF

See Table 7.4.1-1

8

See Table 7.4.1-1

K-factor (K) [dB]

μK

7

N/A

N/A

7

N/A

σK

4

N/A

N/A

4

N/A

Cross-Correlations

ASD vs DS

0

-0.4

0

0.6

0.4

ASA vs DS

0

0

0

0.8

0

ASA vs SF

0

0

0

–0.5

–0.4

ASD vs SF

0

0.6

0

–0.4

0

DS vs SF

-0.5

-0.5

0

–0.8

–0.5

ASD vs ASA

0

0

-0.7

0.4

0

ASD vs

0

N/A

N/A

0

N/A

ASA vs

0

N/A

N/A

0

N/A

DS vs

0

N/A

N/A

-0.5

N/A

SF vs

0

N/A

N/A

0.5

N/A

Cross-Correlations 1)

ZSD vs SF

0.01

-0.04

0

0.2

0

ZSA vs SF

-0.17

-0.25

0

0.3

0

ZSD vs K

0

N/A

N/A

0

N/A

ZSA vs K

-0.02

N/A

N/A

0.1

N/A

ZSD vs DS

-0.05

-0.10

0

0.1

-0.27

ZSA vs DS

0.27

-0.40

0

0.2

-0.06

ZSD vs ASD

0.73

0.42

0.66

0.5

0.35

ZSA vs ASD

-0.14

-0.27

0.47

0

0.23

ZSD vs ASA

-0.20

-0.18

-0.55

0

-0.08

ZSA vs ASA

0.24

0.26

-0.22

0.5

0.43

ZSD vs ZSA

-0.07

-0.27

0

0

0.42

Delay scaling parameter rτ

3.8

1.7

1.7

3.6

3

XPR [dB]

μXPR

12

7

7

11

10

σXPR

4

3

3

4

4

Number of clusters Image

11

10

10

15

19

Number of rays per cluster Image

20

20

20

20

20

Cluster DS (Image) in [ns]

N/A

N/A

N/A

N/A

N/A

Cluster ASD (Image) in [deg]

2

2

2

5

5

Cluster ASA (Image) in [deg]

3

3

3

8

11

Cluster ZSA (Image) in [deg]

3

3

3

9

9

Per cluster shadowing std ζ [dB]

3

3

3

6

3

Correlation distance in the horizontal plane [m]

DS

50

36

36

8

5

ASD

25

30

30

7

3

ASA

35

40

40

5

3

SF

37

120

120

10

6

40

N/A

N/A

4

N/A

ZSA

15

50

50

4

4

ZSD

15

50

50

4

4

fc is carrier frequency in GHz; d2D is BS-UT distance in km.

NOTE 1:    DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.

NOTE 2:    The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.

NOTE 3:    The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.

NOTE 4:    Void.

NOTE 5:    For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution.

NOTE 6:    For InH and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values

 

 

Table 7.5-6 Part-3: Channel model parameters for InF

Scenarios

InF

LOS

NLOS

Delay spread (DS)

lgDS=log10(DS/1s) 4)

μlgDS

log10(26(V/S)+14)-9.35

log10(30(V/S)+32)-9.44

 

σlgDS

0.15

0.19

AOD spread (ASD)

lgASD=log10(ASD/1)

μlgASD

1.56

1.57

σlgASD

0.25

0.2

AOA spread (ASA)

lgASA=log10(ASA/1)

μlgASA

-0.18*log10(1+fc) + 1.78

1.72

σlgASA

0.12*log10(1+fc) + 0.2

0.3

ZOA spread (ZSA)

lgZSA=log10(ZSA/1)

μlgZSA

-0.2*log10(1+fc) + 1.5

-0.13*log10(1+fc) + 1.45

σlgZSA

0.35

0.45

Shadow fading (SF) [dB]

σSF

Specified as part of path loss models

K-factor (K) [dB]

μK

7

N/A

σK

8

N/A

Cross-Correlations

ASD vs DS

0

0

ASA vs DS

0

0

ASA vs SF

0

0

ASD vs SF

0

0

DS vs SF

0

0

ASD vs ASA

0

0

ASD vs

-0.5

N/A

ASA vs

0

N/A

DS vs

-0.7

N/A

SF vs

0

N/A

Cross-Correlations 1)

ZSD vs SF

0

0

ZSA vs SF

0

0

ZSD vs K

0

N/A

ZSA vs K

0

N/A

ZSD vs DS

0

0

ZSA vs DS

0

0

ZSD vs ASD

0

0

ZSA vs ASD

0

0

ZSD vs ASA

0

0

ZSA vs ASA

0

0

ZSD vs ZSA

0

0

Delay scaling parameter rτ

2.7

3

XPR [dB]

μXPR

12

11

σXPR

6

6

Number of clusters Image

25

25

Number of rays per cluster Image

20

20

Cluster DS (Image) in [ns]

N/A

N/A

Cluster ASD (Image) in [deg]

5

5

Cluster ASA (Image) in [deg]

8

8

Cluster ZSA (Image) in [deg]

9

9

Per cluster shadowing std ζ [dB]

4

3

Correlation distance in the horizontal plane [m] 5)

DS

10

10

ASD

10

10

ASA

10

10

SF

10

10

10

N/A

ZSA

10

10

ZSD

10

10

fc is carrier frequency in GHz; d2D is BS-UT distance in km.

NOTE 1:    DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.

NOTE 2:    The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.

NOTE 3:    The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.

NOTE 4:    V = hall volume in m3, S = total surface area of hall in m2 (walls+floor+ceiling)

NOTE 5:    When the simulation scenario is small it is recommended to consider methods to improve the statistical confidence, such as using multiple random seeds in simulations.

 

Table 7.5-6 Part-4: Channel model parameters for SMa (up to 24 GHz)

Scenarios

SMa

LOS

NLOS

O2I

Delay spread (DS)

lgDS=log10(DS/1s)

μlgDS

–7.42

-7.20

-7.20

σlgDS

0.60

0.58

0.58

AOD spread (ASD)

lgASD=log10(ASD/1)

μlgASD

0.48

0.51

0.51

σlgASD

0.27

0.33

0.33

AOA spread (ASA)

lgASA=log10(ASA/1)

μlgASA

1.43

1.63

1.63

σlgASA

0.12

0.26

0.26

ZOA spread (ZSA)

lgZSA=log10(ZSA/1)

μlgZSA

1.18

1.16

1.16

σlgZSA

0.05

0.14

0.14

Shadow fading (SF) [dB]

σSF

See Table 7.4.1-1

See Table 7.4.1-1

8

K-factor (K) [dB]

μK

9

N/A

N/A

σK

7

N/A

N/A

Cross-Correlations

ASD vs DS

0.4

0

0

ASA vs DS

0.8

0.7

0.7

ASA vs SF

–0.5

0

0

ASD vs SF

–0.5

-0.4

-0.4

DS vs SF

–0.4

-0.13

-0.13

ASD vs ASA

0

0

0

ASD vs

0

N/A

N/A

ASA vs

-0.2

N/A

N/A

DS vs

-0.4

N/A

N/A

SF vs

0

N/A

N/A

Cross-Correlations 1)

ZSD vs SF

0

0

0

ZSA vs SF

-0.8

-0.47

-0.47

ZSD vs K

0

N/A

N/A

ZSA vs K

0

N/A

N/A

ZSD vs DS

-0.2

-0.5

-0.5

ZSA vs DS

0

0

0

ZSD vs ASD

0.5

0.5

0.5

ZSA vs ASD

0

-0.1

-0.1

ZSD vs ASA

-0.3

0

0

ZSA vs ASA

0.4

0.36

0.36

ZSD vs ZSA

0

0

0

Delay scaling parameter rτ

2.4

1.5

1.5

XPR [dB]

µXPR

8

4

4

σXPR

4

3

3

Number of clusters N

15

14

14

Number of rays per cluster M

20

20

20

Cluster DS (cDS) in [ns]

max(0.25, 6.5622

-3.4084 log10(fc))

max(0.25, 6.5622

-3.4084 log10(fc))

max(0.25, 6.5622

-3.4084 log10(fc))

Cluster ASD (cASD) in [deg]

2.08

1.33

1.33

Cluster ASA (cASA) in [deg]

5

10

10

Cluster ZSA (cZSA) in [deg]

7

7

7

Per cluster shadowing std ζ [dB]

3

3

3

Correlation distance in the horizontal plane [m]

DS

6

40

40

ASD

15

30

30

ASA

20

30

30

SF

40

50

50

K

10

N/A

N/A

ZSA

15

50

50

ZSD

15

50

50

f c is carrier frequency in GHz; d2D is BS-UT distance in km.

NOTE 1:     DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.

NOTE 2:     The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.

NOTE 3:     The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.

NOTE 4:     Void.

NOTE 5:     For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian distribution and the delay distribution is modelled by an exponential distribution.

 

Table 7.5-7: ZSD and ZOD offset parameters for UMa

Scenarios

LOS

NLOS

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

max[-0.5, -2.1(d2D/1000)

-0.01 (hUT - 1.5)+0.75]

max[-0.5, -2.1(d2D/1000)

-0.01(hUT - 1.5)+0.9]

σlgZSD

0.40

0.49

ZOD offset

µoffset,ZOD

0

e(fc)-10^{a(fc) log10(max(b(fc), d2D))

+c(fc) -0.07(hUT-1.5)}

Note:    For NLOS ZOD offset:

a(fc) = 0.208log10(fc)- 0.782;

b(fc) = 25;

c(fc) = -0.13log10(fc)+2.03;

e(fc) = 7.66log10(fc)-5.96.

 

Table 7.5-8: ZSD and ZOD offset parameters for UMi – Street Canyon

Scenarios

LOS

NLOS

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

max[-0.21, -14.8(d2D/1000)

+ 0.01|hUT-hBS| + 0.83]

max[-0.5, -3.1(d2D/1000)

+ 0.01 max(hUT-hBS,0) +0.2]

σlgZSD

0.35

0.35

ZOD offset

µoffset,ZOD

0

-10^{-1.5log10(max(10, d2D))+3.3}

 

Table 7.5-9: ZSD and ZOD offset parameters for RMa

Scenarios

LOS

NLOS

O2I

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

max[-1, -0.17(d2D/1000)

-0.01(hUT - 1.5) + 0.22]

max[-1, -0.19(d2D/1000)

-0.01(hUT - 1.5) + 0.28]

max[-1, -0.19(d2D/1000)

-0.01(hUT - 1.5) + 0.28]

σlgZSD

0.34

0.30

0.30

ZOD offset

µoffset,ZOD

0

arctan((35 - 3.5)/d2D)

-arctan((35 - 1.5)/d2D)

arctan((35 - 3.5)/d2D)

-arctan((35 - 1.5)/d2D)

 

Table 7.5-10: ZSD and ZOD offset parameters for Indoor-Office

Scenarios

LOS

NLOS

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

-1.43 log10(1+ fc)+2.228

1.08

σlgZSD

0.13 log10(1+fc)+0.30

0.36

ZOD offset

µoffset,ZOD

0

0

 

Table 7.5-11: ZSD and ZOD offset parameters for InF

Scenarios

LOS

NLOS

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

1.35

1.2

σlgZSD

0.35

0.55

ZOD offset

µoffset,ZOD

0

0

 

Table 7.5-12: ZSD and ZOD offset parameters for SMa

Scenarios

LOS

NLOS

O2I

ZOD spread (ZSD)

lgZSD=log10(ZSD/1)

μlgZSD

0.14

0.14

0.14

σlgZSD

0.16

0.16

0.16

ZOD offset

µoffset,ZOD

0

atanhBS-hcld2D-atanhBS-hUTd2DhUT<hcl0hUThcl

hcl= 3.5

 

Notes for Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10, 7.5-11, 7.5-12:

NOTE 1:    fc is center frequency in GHz; d2D is BS-UT distance in m.

NOTE 2:    hBS and hUT are antenna heights in m for BS and UT respectively.

NOTE 3:    The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X)}) is used for logarithmized parameters X.

NOTE 4:    For frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent ZSD and ZOD offset parameters in Table 7.5-7 and 7.5-10

NOTE 5: The ZSD parameters for O2I links are the same parameters that are used for outdoor links, depending on the LOS condition of the outdoor link part in Tables 7.5-7 and 7.5-8.

7.6     Additional modelling components #

7.6.0     Introduction of additional modelling components #

The additional modelling components in this clause are to support advanced simulations, such as simulations with very large arrays and large bandwidth, simulations affected by oxygen absorption (frequencies between 53 and 67 GHz), simulations in which spatial consistency is important (e.g. high number of closely located user), simulations of mobility, simulations of blockage effects, simulations of absolute time of arrival, simulations of dual mobility, simulations that include sources of EM interference, simulations of devices embedded in machinery or enclosures, simulations of near-field channel effects (i.e. characteristics of spherical wavefront), simulations of spatial non-stationarity (i.e. antenna element-wise power variation), simulation of number of cluster variability, and simulation of polarization power variability. These modelling components affect some of the steps between Step 1 and Step 12 in Clause 7.5.

These extensions are computationally more expensive and might not be required in all evaluation cases.

7.6.1     Oxygen absorption #

Oxygen absorption loss is applied to the cluster responses generated in Step 11 in Clause 7.5. The additional loss, OLn(fc) for cluster n at centre frequency fc is modelled as:

    Image [dB]    (7.6-1)

where:

-    α(fc) is frequency dependent oxygen loss [dB/km] characterized in Table 7.6.1-1;

-    c is the speed of light [m/s]; and d3D is the distance [m];

-    τn is the n-th cluster delay [s] in Step 11 in Clause 7.5;

-    τΔ is 0 in the LOS case and min(Image) otherwise, where min(Image) is the minimum delay in Step 5.

For centre frequencies not specified in this table, the frequency dependent oxygen loss α(fc) is obtained from a linear interpolation between two loss values corresponding to the two adjacent centre frequencies of the centre frequency fc.

Table 7.6.1-1 Frequency dependent oxygen loss α(f) [dB/km]

f in [GHz]

0-52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68-100

α(f) in [dB/km]

0

1

2.2

4

6.6

9.7

12.6

14.6

15

14.6

14.3

10.5

6.8

3.9

1.9

1

0

 

For large channel bandwidth, first transform the time-domain channel response of each cluster (all rays within one cluster share common oxygen absorption loss for simplicity) into frequency-domain channel response, and apply the oxygen absorption loss to the cluster's frequency-domain channel response for frequency fc + Δf within the considered bandwidth. The oxygen loss, OLn(fc+ Δf) for cluster n at frequency fc+ Δf is modelled as:

    Image [dB]    (7.6-2)

where:

-    α(fc+ Δf) is the oxygen loss (dB/km) at frequency fc+ Δf characterized in Table 7.6.1-1. Note that Δf is in [-B/2, B/2], where B is the bandwidth. Linear interpolation is applied for frequencies not provided in Table 7.6.1-1.

The final frequency-domain channel response is obtained by the summation of frequency-domain channel responses of all clusters.

Time-domain channel response is obtained by the reverse transform from the obtained frequency-domain channel response.

7.6.2     Large bandwidth and large antenna array #

7.6 .2.1    Modelling of the propagation delay #

The modelling in this clause applies only when the bandwidth B is greater than c/D Hz, where

-    D is the maximum antenna aperture in either azimuth or elevation (m)

-    c is the speed of light (m/s)

Each ray within a cluster for a given u (Rx) and s (Tx) has unique time of arrival (TOA). The channel coefficient generation step (Step 11 in Clause 7.5) is updated to model individual rays. In this case, the channel response of ray m in cluster n for a link between Rx antenna u and Tx antenna s at delay Image at time t is given by:

    Image    (7.6-3)

with Image is the wavelength on frequency Image, which can be implemented by user's own method. The delay (TOA) for ray m in cluster n for a link between Rx antenna u and Tx antenna s is given by:

    Image    (7.6-4)

Note that Equation (7.6-3) only considers the delays Image intentionally. If unequal ray powers are considered, Image are generated according to Clause 7.6.2.2. Otherwise, ray powers are equal within a cluster, i.e., Image for all m. Note: this model is developed assuming plane wave propagation.

7.6 .2.2    Modelling of intra-cluster angular and delay spreads #

With large antenna arrays or large bandwidths, the angle and/or delay resolution can be larger than what the fast fading model in Clause 7.5 is designed to support. To model this effect, the following modifications to Step 7 in Clause 7.5 can be optionally used.

1.    The offset angles αm in (7.5-13), (7.5-18) and (7.5-20) are generated independently per cluster and ray using:

    Image    (7.6-5)

where Image denotes the continuous uniform distribution in the interval Image. These random variables may further be modelled as spatially consistent with correlation distance equal to the cluster-specific random variable correlation distance of Table 7.6.3.1-2.

2.    The relative delay of m-th ray is given by Image, where n is the cluster index, Image, the cluster DS Image is given in Table 7.5-6. These random variables may further be modelled as spatially consistent with correlation distance equal to the cluster-specific random variable correlation distance of Table 7.6.3.1-2. In this case, the sub-cluster mapping according to (7.5-26) and Table 7.5-5 shall not be applied. The delays to be used in Equation (7.6-3) are given by Image.

3.    Ray powers are determined unequally by the following process:

The power of m-th ray in n-th cluster is given by Image for m = 1,…,M, where

    Image    (7.6-6)

and Image, Image, Image, and Image are respectively the intra-cluster delay spread and the corresponding intra-cluster angular spread that are given in Table 7.5-6. The cluster zenith spread of departure is given by

    Image,    (7.6-7)

with Image being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.

4.    The number of rays per cluster shall be calculated as follows:

    M=minmaxMtMAODMZOD,Mmin,Mmax    (7.6-8)

where:

-    Image

-    Image

-    Image

-    Mmin can be chosen based on the mean of dominant rays across all clusters for a specific frequency and deployment scenario. For example, the number of dominant rays in a cluster is defined as the minimum number of rays that contain 95% of the total cluster power, when the rays in a cluster are sorted in descending order of power. The default value of Mmin = 20 is assumed.

-    Image is the upper limit of Image, and it should be selected by the user of channel model based on the trade-off between simulation complexity and accuracy.

-    Image and Image are the array size in m in horizontal and vertical dimension, B is bandwidth in Hz, Image and Image are the cluster spreads in degrees, and Image is the wavelength.

-    Image is a "sparseness" parameter with value 0.5.

It is noted that each MPC may have different AOD, ZOD, and delay.

7.6 .3    Spatial consistency #

7.6 .3.1    Spatial consistency procedure #

A new procedure, namely a spatial consistency procedure, can be used for both cluster-specific and ray-specific random variables to be generated in various channel generation steps in Clause 7.5, so that they are spatially consistent for drop-based simulations. Alternatively, this can be used together with Procedure B described in Clause 7.6.3.2 for spatially consistent mobility simulations.

The procedure can be considered as a 2D random process (in the horizontal plane) given the UT locations based on the parameter-specific correlation distance values for spatial consistency, specified in Table 7.6.3.1-2. The cluster specific random variables include:

-    Cluster specific random delay in Step 5;

-    Cluster specific shadowing in Step 6; and

-    Cluster specific offset for AOD/AOA/ZOD/ZOA in Step 7.

-    Cluster specific sign for AOD/AOA/ZOD/ZOA in Step 7.

-    Optionally in case of large bandwidth as described in Clause 7.6.2.2 the procedure may apply as well for the parameters of rays within a cluster.

The procedure shall apply to each cluster before sorting the delay. Cluster specific sign for AOD/AOA/ZOD/ZOA in Step 7 shall be kept unchanged per simulation drop even if UT position changes during simulation. The ray specific random variables include:

-    Random coupling of rays in Step 8;

-    XPR in Step 9; and

-    Random phase in Step 10.

The random coupling of rays in Step 8 and the intra-cluster delays in Step 11 shall be kept unchanged per simulation drop even if UT position changes during simulation.

Table 7.6.3.1-2 Correlation distance for spatial consistency

Correlation distance in [m]

RMa

UMi

UMa

SMa

Indoor

InF

LOS

NLOS

O2I

LOS

NLOS

O2I

LOS

NLOS

O2I

LOS

NLOS

O2I

Cluster and ray specific random variables

50

60

15

12

15

15

40

50

15

40

50

15

10

10

LOS/NLOS state

60

50

50

50

10

dclutter2

Indoor/outdoor state

50

50

50

50

N/A

N/A

 

7.6.3.2     Spatially-consistent UT/BS mobility modelling #

For mobility simulation enhancement, two alternative spatial consistency procedures – Procedure A and Procedure B – are described as follows. The procedures presented below consider the downlink direction same as in Clause 7.5.

Procedure A:

For Image when a UT/BS is dropped into the network, spatially consistent powers/delays/angles of clusters are generated according to Clause 7.6.3.1.

The updated distance of UT/BS should be limited within 1 meter, i.e. when Image, and the updated procedure in the following should take the closest realization instead of Image. In case the absolute time of arrival modeling of clause 7.6.9 is simultaneously used, the update distance should be the minimum of 1 meter and d3D10.

At Image, update channel cluster power/delay/angles based on UT/BS channel cluster power/delay/angles, moving speed moving direction and UT position at Image.

Cluster delay is updated as:

    τntk=τntk-1-rrx,ntk-1Tvrxtk-1+rtx,ntk-1Tvtxtk-1ctfor k>0τnt0+τt0+d3D(t0)cfor k=0    (7.6-9)

where

Image is the speed of light in [m/s];

vtxtk and vrxtk are the BS and UTvelocity vectors respectively, with vtxtk2=vtx and vrxtk2=vrx;

Image is the 3D distance between Rx antenna and Tx antenna at Image in [m];

Image are the cluster delays in [s] as in Step 11 in Clause 7.5;

Image, where Image are the delays from Equation (7.5-1). In case the absolute time of arrival modelling of clause 7.6.9 is simultaneously used, Image where Δτ is determined according to clause 7.6.9.

 

Also,

    Image    (7.6-10)

where Image and Image are cluster specific zenith and azimuth angles of arrival.

    rtx,ntk-1=sinθn,ZODtk-1cosϕn,AODtk-1sinθn,ZODtk-1sinϕn,AODtk-1cosθn,ZODtk-1    (7.6-10aa)

where θn,ZOD and ϕn,AOD are the cluster specific zenith and azimuth angles of departure.

After updating the delays according to equation (7.6-9), the delays over the mobility range are normalized. Equation (7.5-2) of the fast fading model is replaced by

    Image    (7.6-10a)

in which Image covers the entire duration of the mobility model.

Cluster powers are updated as in Step 6 using the cluster delays from Equation (7.6-10a).

Cluster departure angles (Image and Image) and arrival angles (Image and Image) are updated using a cluster-wise transformation of the UT/BS velocity vector given by

    vn,rx'(tk)=vrxtk-vtx(tk)for LOSRn,rxvrx(tk)-vtx(tk)for NLOS    (7.6-10b)

    vn,tx'(tk)=vtxtk-vrx(tk)for LOSRn,txvtx(tk)-vrx(tk)for NLOS    (7.6-10c)

with

    Rn,rx=RZϕn,AODtk+πRYπ2-θn,ZODtk1000Xn0001RYπ2-θn,ZOAtkRZ-ϕn,AOAtk,

and

    Rn,tx=RZ-ϕn,AODtkRYπ2-θn,ZODtk1000Xn0001RYπ2-θn,ZOAtkRZϕn,AOAtk+π,

where Image and Image being the rotation matrices around y-axis and z-axis as defined in Equation (7.1-2). Random variable Image is sampled from a uniform distribution on a NLOS cluster basis and is not changed during the UT mobility within a drop. The cluster specific decorrelation distances are 60m, 15m, 50m and 10m for RMa, UMi, UMa and Indoor scenarios, respectively.

Now, the departure and arrival angles in radians are updated as:

    ϕn,AODtk=ϕn,AODtk-1+vn,rx'tk-1Tϕθn,ZODtk-1,ϕn,AODtk-1cτntk-1sinθn,ZODtk-1t    (7.6-11)

    θn,ZODtk=θn,ZODtk-1+vn,rx'tk-1Tθθn,ZODtk-1,ϕn,AODtk-1cτntk-1t    (7.6-12)

and

    ϕn,AOAtk=ϕn,AOAtk-1+vn,tx'tk-1Tϕθn,ZOAtk-1,ϕn,AOAtk-1cτntk-1sinθn,ZOAtk-1t    (7.6-13)

    θn,ZOAtk=θn,ZOAtk-1+vn,tx'tk-1Tθθn,ZOAtk-1,ϕn,AOAtk-1cτntk-1t,    (7.6-14)

with Image and Image being the spherical unit vectors defined in Equations (7.1-13) and (7.1-14).

 

Procedure B:

In procedure B, spatial or time evolution of the channel is obtained by generating channel realizations separately for all links to different Rx positions using Steps 1-12 of Clause 7.5 together with the spatially consistent procedure of Clause 7.6.3.1. In the case of mobility these positions may be a function of time along one or more Rx trajectories. Furthermore, to ensure that the spatial or time evolution of delays and angles are within reasonable limits, Steps 5, 6, and 7 in Clause 7.5 should be replaced by the below procedure.

Note: For implementation purposes, LSPs and SSPs may be interpolated within the coherence length or time of the respective parameter.

 

Step 5: Generate cluster delays τImage, with Image.

Image delays are drawn randomly from a uniform distribution.

    Image    (7.6-15)

Normalise the delays by subtracting the minimum delay:    Image. The autocorrelation distance for Image is Image.

In the case of LOS, set the delay of the first cluster Image to 0.

Step 6: Generate Image arrival angles and departure angles for both azimuth and elevation using (7.6-16) instead of (7.5-9) and (7.5-14).

    Image    (7.6-16)

with Image. This step is repeated independently for AOD, AOA, ZOD, and ZOA with corresponding maximum angles for the uniform distribution. In case of LOS, set the angles of the first cluster (Image, etc) to 0.

The autocorrelation distances are τmaxc0 Image for AOD, and ZOD, while they are fixed to 50m for AOA, and ZOA.

Offset angles etc are applied in the modified Step 7b below after cluster powers have been calculated.

Step 7: Generate cluster powersP Image

Cluster powers are calculated assuming a single slope exponential power profile and Laplacian angular power profiles. The cluster powers are determined by

    Image    (7.6-17)

where Image (autocorrelation distance same as for shadow fading) is the per cluster shadowing term in [dB]. Delay spread Image and angular spreads Image, Image, Image, Imageare generated in Step 4 of Clause 7.5. Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,

    Image    (7.6-17a)

In the case of LOS condition, substitute Image with Image and {Image, Image, Image, Image} with {Image, Image, Image, Image} respectively to preserve the delay and angular spreads. Image is the Ricean K-factor as generated in Step 4 converted to linear scale. Furthermore, an additional specular component is added to the first cluster. Power of the single LOS ray is:

    Image    (7.6-17b)

and the cluster powers are not normalized as in equation (7.6-17a), but:

    Image    (7.6-17c)

where δ(.) is Dirac's delta function.

Assign the power of each ray within a cluster as P/ M, where M is the number of rays per cluster.

 

Step 7b: Apply offset angles

The ray AOA angles are determined by

    Image    (7.6-17d)

where Image is the LOS direction defined in the network layout description, see Step1c.

The generation of AOD (Image) follows a procedure similar to AOA as described above.

The ZOA angles are determined by

    Image    (7.6-17e)

where Image if the BS-UT link is O2I and Imageotherwise. The LOS direction is defined in the network layout description, see Step1c.

The ZOA angles are determined by

    Image    (7.6-17f)

where Imageis the mean of the ZSD log-normal distribution.

 

Some of the delay and angle spreads and standard deviations used in equations (7.6-15) and (7.6-16) may be frequency-dependent. In the case of multi-frequency simulations according to Clause 7.6.5, the largest value among all the simulated frequencies should be used in (7.6-15) and (7.6-16) so that the cluster and ray delays and angles (but not the powers or the resulting delay or angular spreads) are the same for all frequencies.

7.6 .3.3    LOS/NLOS, indoor states and O2I parameters #

The LOS state can be determined according to the spatial consistency procedure for random variables as mentioned in Clause 7.6.3.1, by comparing a realization of a random variable generated with distance-dependent LOS probability. If the realization is less than the LOS probability, the state is LOS; otherwise NLOS. Decision of LOS and NLOS status should be used in Step 2 in Clause 7.5 if this advanced simulation is performed.

The same procedure can be applied for determining the indoor state, with the indoor probability instead of the LOS probability.

The correlation distance for LOS state and indoor/outdoor is specified in Table 7.6.3.1-2.

The indoor distance can be modeled as the minimum of two spatially consistent uniform random variables within (0, 25) meters with correlation distance 25m.

Note in case the UT is in an indoor state, the pathloss model changes and a penetration loss is considered. For details on the model, see Clause 7.4.3. Here, the focus is on modelling aspects with respect to spatial consistency. As described in Clause 7.4.3, the penetration loss deviation σp represents variations within and between buildings of the same type. For spatial consistency this can be modeled as a spatially consistent random variable with correlation distance 10m, see Clause 7.6.3.1. The "building type" is determined using a spatially consistent uniform random variable with correlation distance 50 m. The building type is determined by comparing the random variable with P1, where P1 is the probability of the building type with low loss penetration. If the realization of the random variable is less than P1, the building type is low loss; otherwise the building type is high loss.

The cluster-specific and ray-specific random variables as defined in Clause 7.6.3.1 on the same floor are generated in the spatial consistency modelling; otherwise, these variables across different floors are uncorrelated.

In case there is a transition from LOS to NLOS due to UT mobility, there will be a hard transition in the channel response. This is because pathloss and LS parameters are different for these states, leading automatically to very different channel realizations. To circumvent such hard transitions the optional soft LOS state can be considered to determine the PL and the channel impulse responses containing characteristics of both LOS and NLOS. Soft LOS state LOSsoftImage is generated by floating numbers between 0 (NLOS) and 1 (LOS) in the spatial consistency modelling. The value of LOSsoft Imageis determined by

    Image,    (7.6-18)

LOSsoft=12+1πarctandLOSλG+F(d)where:

-    GImage is a spatially consistent Gaussian random variable with correlation distance according to Table 7.6.3.1-2;

-    ImageFd=2erf-12PLOSd-1; and

-    ImagePLOSd is the distance dependent LOS probability function.

After LOSsoft Imageis obtained, Steps 2-12 of the channel coefficient generation described in Clause 7.5 are performed twice, once with the propagation condition in Step 2 set as LOS and once with the propagation condition in Step 2 set as NLOS. The resulting channel coefficients are denoted as Image and Image respectively, where Image is generated with the LOS path loss formula and channel model parameters while is Image generated using the NLOS path loss formula and channel model parameters. The channel matrix Image with soft LOS state is determined from a linear combination of Image and Image as:

    Image    (7.6-19)

It is noted that soft indoor/outdoor states are not modelled in this TR. Thus the model does not support transitions between indoor/outdoor states in mobility simulations.

7.6.3.4     Applicability of spatial consistency #

According to the characteristics of different cluster specific parameters and ray specific parameters, the following types on spatial consistency are defined:

-    Site-specific: parameters for different BS-UT links are uncorrelated, but the parameters for links from co-sited sectors to a UT are correlated.

-    All-correlated: BS-UT links are correlated.

In Table 7.6.3.4-1, correlation type for each large scale parameter, cluster specific parameter and ray specific parameter is clarified.

Table 7.6.3.4-1: Correlation type among TRPs

Parameters

Correlation type

Delays

Site-specific

Cluster powers

Site-specific

AOA/ZOA/AOD/ZOD offset

Site-specific

AOA/ZOA/AOD/ZOD sign

Site-specific

Random coupling

Site-specific

XPR

Site-specific

Initial random phase

Site-specific

LOS/NLOS states

Site-specific

Blockage (Model A)

All-correlated

O2I penetration loss

All-correlated

Indoor distance

All-correlated

Indoor states

All-correlated

 

Spatial consistency is not modelled in the following situations:

-    Different link types, e.g., outdoor LOS, outdoor NLOS or O2I

-    UE locates on different floors

Table 7.6.3.4-2, lists conditions in which each large scale parameter, cluster specific parameter and ray specific parameter is assumed not spatially correlated.

Table 7.6.3.4-2: Conditions when parameters are not spatially correlated.

Parameters

Uncorrelated states

Delays

Outdoor LOS/outdoor NLOS/O2I (different floors)

Cluster powers

Outdoor LOS/outdoor NLOS/O2I (different floors)

AOA/ZOA/AOD/ZOD offset

Outdoor LOS/outdoor NLOS/O2I (different floors)

AOA/ZOA/AOD/ZOD sign

Outdoor LOS/outdoor NLOS/O2I (different floors)

Random coupling

Outdoor LOS/outdoor NLOS/O2I (different floors)

XPR

Outdoor LOS/outdoor NLOS/O2I (different floors)

Initial random phase

Outdoor LOS/outdoor NLOS/O2I (different floors)

Blockage

Outdoor/O2I (different floors)

Standard deviation for O2I penetration loss

Different building types, i.e., high/low loss

 

7.6 .4    Blockage #

Blockage modelling is an add-on feature to the channel model. The method described in the following applies only when this feature is turned on. In addition, the temporal variability of the blockage modelling parameters is on-demand basis. It is also noted that the modelling of the blockage does not change LOS/NLOS state of each link.

When blockage model is applied, the channel generation in Clause 7.5 should have several additional steps between Step 9 and 10 as illustrated in Figure 7.6.4-1.

 

Image

Figure.7.6.4-1 Channel generation procedure with blockage model

Two alternative models (Model A and Model B) are provided for the blockage modelling. Both approaches have their own use cases. Model A is applicable when a generic and computationally efficient blockage modelling is desired. Model B is applicable when a specific and more realistic blocking modelling is desired.

7.6 .4.1    Blockage model A #

Model A adopts a stochastic method for capturing human and vehicular blocking.

Step a: Determine the number of blockers.

Multiple 2-dimensional (2D) angular blocking regions, in terms of centre angle, azimuth and elevation angular span are generated around the UT. There is one self-blocking region, and Kblock = 4 non-self-blocking regions, where Kblock may be changed for certain scenarios (e.g., higher blocker density). Note that the self-blocking component of the model is important in capturing the effects of human body blocking.

Step b: Generate the size and location of each blocker.

For self-blocking, the blocking region in UT LCS is defined in terms of elevation and azimuth angles, (Image,Image) and azimuth and elevation angular span (Image, Image).

    Image    (7.6-20)

where the parameters are described in Table 7.6.4.1-1.

Table 7.6.4.1-1: Self-blocking region parameters.

 

Image

Image

Image

Image

Portrait mode

260o

120o

100o

80o

Landscape mode

40o

160o

110o

75o

 

For non-self-blocking k = 1, …, 4, the blocking region in GCS is defined by

    Image    (7.6-21)

where the parameters are described in Table 7.6.4.1-2, as well as the distance r between the UT and the blocker.

Table 7.6.4.1-2: Blocking region parameters.

Blocker index (k = 1, …, 4)

Image

Image

Image

Image

r

InH scenario

Uniform in

[0o, 360o]

Uniform in

[15o, 45o]

90o

Uniform in

[5o, 15o]

2 m

UMi, UMa, SMa, RMa scenarios

Uniform in

[0o, 360o]

Uniform in

[5o, 15o]

90o

5o

10 m

 

Step c: Determine the attenuation of each cluster due to blockers.

The attenuation of each cluster due to self-blocking corresponding to the centre angle pair (Image,Image), is 30 dB provided that φAoAφ1<x12Image and θZoAθ1<y12.Image. Otherwise, the attenuation is 0 dB.

The attenuation of each cluster due to the non-self-blocking regions (k=1, …, 4) is given by

    Image    (7.6-22)

provided that φAoAφk<xkImage and θZoAθk<ykImage. Otherwise, the attenuation is 0 dB. The terms in the above equation are given as

    Image    (7.6-23)

where

    Image,    (7.6-24)

    Image,    (7.6-25)

    Image,    (7.6-26)

    Image    (7.6-27)

In the above formula for Image, Image is the wavelength. The appropriate signs (Image)within the Image term are described in Table 7.6.4.1-3.

 

Table 7.6.4.1-3: Description of signs

 

Image

Image

Image

Image

(-, +) for Image

(+, -) for Image

(-, +) for ImageA1,A2

(+, +) for Image

(-, +) for A1,A2Image

(-, +) for Z1,Z2Image

Image

(+, +) for ImageA1,A2

(+, -) for Image

(+, +) for ImageA1,A2

(+, +) for Z1,Z2Image

(+, +) for A1,A2Image

(-, +) for Z1,Z2Image

Image

(+, -) for A1,A2Image

(+, -) for Z1,Z2Image

(+, -) for A1,A2Image

(+, +) for Z1,Z2Image

(+, -) for A1,A2Image

(-, +) for Z1,Z2Image

 

Step d: Spatial and temporal consistency of each blocker.

The centre of the blocker is generated based on a uniformly distributed random variable, which is temporally and spatially consistent. The two-dimensional autocorrelation function R(x,t)Image can be described with sufficient accuracy by the exponential function

    Image    (7.6-28)

The spatial correlation distance dcorrImage for the random variable determining the centre of the blocker is given in Table 7.6.4.1-4 for different scenarios.

Table 7.6.4.1-4: Spatial correlation distance for different scenarios.

Spatial correlation distance Image in [m] for the random variable determining the centre of the blocker

UMi/UMa/SMa/RMa

InH

LOS

NLOS

O2I

LOS

NLOS

10

10

5

5

5

 

The correlation timetcorr=dcorr/v is defined by Image, where vImage is the speed of the moving blocker.

Note: The rectangular blocker description is chosen for self-blocking region with the specific choices of φ1,θ1(Image, Image) assumed here. Generalization of this description to other choices should be done with care as the rectangular description may not be accurate.

7.6 .4.2    Blockage model B #

Model B adopts a geometric method for capturing e.g., human and vehicular blocking.

Step a: Determine blockers

A number, Kblock, of blockers are modelled as rectangular screens that are physically placed on the map. Each screen has the dimension by height (Image) and width (Image), with the screen centre at coordinate Image.

Note:

-    The number of blockers (Kblock), their vertical and horizontal extensions (Image and Image), locations Image, density, and movement pattern (if non-stationary) are all simulation assumptions, to allow different blocking scenarios to be constructed depending on the need of the particular simulation study.

    Recommended parameters for typical blockers are provided in Table 7.6.4.2-5.

-    The blocking effect diminishes with increasing distance to the blocker. For implementation purposes it may be sufficient to consider only the Kblock nearest blockers or the blockers closer than some distance from a specific UT.

Table 7.6.4.2-5: Recommended blocker parameters

 

Typical set of blockers

Blocker dimensions

Mobility pattern

Indoor; Outdoor; InF

Human

Cartesian: w=0.3m; h=1.7m

Stationary or up to 3 km/h

Outdoor

Vehicle

Cartesian: w=4.8m; h=1.4m

Stationary or up to 100 km/h

InF

AGV

Cartesian: w=3m; h=1.5m

Up to 30 km/h

InF

Industrial robot

Cartesian: w=2m; h=0.2m

Up to 3 m/s

 

Step b: Determine the blockage attenuation per sub-path

Attenuation caused by each blocker to each sub-path is modelled using a simple knife edge diffraction model and is given by

    Image    (7.6-29)

where Fh1Image, Fh2Image and Fw1Image, Fw2Image account for knife edge diffraction at the four edges, and are given by

    Image    (7.6-30)

where λImage is the wave length. As shown in Figure 7.6.4.2-2, D1h1h2w1|w2Image are the projected (onto the side and top view planes) distances between the receiver and four edges of the corresponding blocker, and D2h1h2w1|w2 Imageare the projected (onto the side and top view planes) distances between the transmitter and four edges of the corresponding blocker. The side view plane is perpendicular to the horizontal ground plane. The top view is perpendicular to the side view. For each cluster, the blocker screen is rotated around its centre such that the arrival direction of the corresponding path is always perpendicular to the screen. It should be noted that different rotations are required for each individual sub-path. Meanwhile, the base and top edges of the screens are always parallel to the horizontal plane. As the screen is perpendicular to each sub-path, Image is the distance between the transmitter and receiver for the direct path in LOS, and Image is the distance between the blocker screen and receiver, projected onto the incoming sub-path direction, for all the other (NLOS) paths. In the equation of Image, the plus and minus signs are determined in such a way that, as shown in Figure 7.6.4.2-2,

-    if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in side view, minus sign is applied for the shortest path among Imageand Image in the NLOS case (Imageand Imagein the LOS case) and plus sign is applied for the other edge.

-    if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in top view, minus sign is applied for the shortest path among Imageand Image in the NLOS case (Imageand Imagefor the LOS case) and plus sign is applied for the other edge.

-    if the sub-path intersects the screen plus signs are applied for both edges.

For the case of multiple screens the total loss is given by summing the losses of each contributing screen in dB units.

The model according to option B is consistent in time, frequency and space, and is more appropriate to be used for simulations with arbitrarily designated blocker density.

 

Image

Figure 7.6.4.2-2(a): Illustration of the geometric relation among blocker, receiver and transmitter for the LOS path

 

Image

Figure 7.6.4.2-2(b): Illustration of the geometric relation between blocker and receiver for NLOS paths

7.6 .5    Correlation modelling for multi-frequency simulations #

This clause describes how to generate parameters to reflect correlation across different frequencies for a BS-UT link, for simulations involved with multiple frequencies.

For those simulations, the steps in Clause 7.5 should be revised according to the following:

-    The parameters generated in Step 1 are the same for all the frequencies, except for antenna patterns, array geometries, system center frequency and bandwidth.

-    Propagation conditions generated in Step 2 are the same for all the frequencies. It is noted that soft LOS states may be different due to frequency dependent function.

-    The parameters generated in Step 4 are the same for all the frequencies, except for possibly frequency-dependent scaling of e.g. delay spread and angular spreads according to the LSP tables. I.e. let x be a random variable drawn from a Gaussian distribution: x ~ N(0,1). Then the delay spread at frequency f is DS(f)= 10^(μlgDS(f) + σlgDS(f)x), where the same value of x is used for all frequencies. A corresponding procedure applies to each of the angular spreads.

-    The cluster delays and angles resulting from Steps 5-7 are the same for all frequency bands

-    Per-cluster shadowing n in Step 6 are independently generated for the frequency bands.

-    Cluster powers in Step 6 may be frequency-dependent.

-    Steps 8-11 are independently applied for the frequency bands.

In addition, when blockage is modeled according to Clause 7.6.4, the positions of blockers are the same across all the frequencies.

Note:    The requirements above may not be fully aligned with the behavior of the model according to Clause 7.5, since cluster delays and angles will be frequency-dependent in scenarios where the DS or AS is frequency-dependent. The procedure below may alternatively be used to ensure that cluster delays and angles are frequency-independently generated.

7.6.5.1     Alternative channel generation method #

The alternative method replaces Steps 5-7 in Clause 7.5 with the below Steps 5'-7'. The inputs to the alternative method are the delay and angular spreads determined according to Step 4 at an anchor frequency, e.g. 2 GHz: DS0, ASD0, ASA0, ZSD0, ZSA0, the delay and angular spreads determined according to Step 4 at a frequency of interest: DS, ASD, ASA, ZSD, ZSA, and the number of clusters N from Table 7.5-6.

Step 5': Generate nominal delays Image and angles Image, Image, Image, Image.

Generate N delays from a single-sided exponential distribution with zero mean and standard deviation of Image, according to Image with Image.

Generate N AODs from a wrapped Gaussian distribution with zero mean and standard deviation of Image, according to Image with Image.

Generate N AOAs from a wrapped Gaussian distribution with zero mean and standard deviation of Image, according to Image with Image.

Generate N ZODs from a wrapped Laplacian distribution with zero mean and standard deviation of Image, according to Image with Image.

Generate N ZOAs from a wrapped Laplacian distribution with zero mean and standard deviation of Image, according to Image with Image.

r is a proportionality factor, r=1.5. The principal value of the arg function should be used, e.g. (-180,180).

In case of LOS, set Image, Image, Image, Image, and Image.

Step 6': Generate cluster powers Image.

Generate cluster powers as

    Image    (7.6.30a)

where Image is the per cluster shadowing term in [dB] and

    Image    (7.6.30b)

    Image    (7.6.30c)

    Image    (7.6.30d)

    Image    (7.6.30e)

    Image    (7.6.30f)

Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,

    Image    (7.6.30g)

or, in the case of LOS, so that

    Image    (7.6.30h)

where KRImage is the K-factor converted to linear scale.

 

Step 7': Generate delays Image and angles Image, Image, Image, Image.

    Image    (7.6.30i)

 

    Image    (7.6.30j)

 

    Image    (7.6.30k)

 

    Image    (7.6.30l)

 

    Image    (7.6.30m)

 

Repeat Steps 6'-7' for each frequency of interest, reusing the delays and angles from Step 5' for all frequencies.

Note: The resulting delay and angular spreads of channels generated with this alternative method will be similar but not identical to when using Steps 5-7 in Clause 7.5.

7.6 .6    Time-varying Doppler shift #

The Doppler shift generally depends on the time evolution of the channel as it is defined as the derivative of the channels phase over time. It can be the result from Tx, Rx, or scatterer movement. The more general form of the exponential Doppler term as used in Equation (7.5-22) is given by

    Image.    (7.6-31)

Here, Image is the normalized vector that points into the direction of the incoming wave as seen from the Rx at time Image. Image denotes the velocity vector of the Rx at time Image, while Image denotes a reference point in time that defines the initial phase, e.g. Image.

Note that Equation (7.5-22) only holds for time-invariant Doppler shift, i.e. Image.

7.6 .7    UT rotation #

UT rotation modelling is an add-on feature. When modelled, Step 1 in Clause 7.5 shall consider UT rotational motion.

Step 1:

Add: h) Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle.

7.6 .8    Explicit ground reflection model #

In case the ground reflection shall be modelled explicitly, Equation (7.5-30) has to be replaced by

    Image    (7.6-32)

with the delays for the ground reflected and the LOS paths being defined by their lengths, according to the Tx-Rx separation Image and the Tx and Rx heights Image and Image respectively, as

    Image,    (7.6-33)

and

    Image.    (7.6-34)

The channel coefficient for the ground reflected path is given by

    Image    (7.6-35)

with the normalized vectors pointing towards the ground reflection point from the Tx

    Image    (7.6-36)

and from the Rx side

    Image.    (7.6-37)

The angles for the ground reflected path are given by the geometry assuming a flat surface with its normal pointing into z-direction. The angles at the Tx side can be determined by

    Image    (7.6-37a)

    Image    (7.6-37b)

and at the Rx side by

    Image    (7.6-37c)

    Image.    (7.6-37d)

The reflection coefficients for parallel and perpendicular polarization on the ground, cf. [21], are given by

    Image,    (7.6-38)

and

    Image,    (7.6-39)

with the complex relative permittivity of the ground material given by

    Image.    (7.6-40)

The electric constant Image is given by 8.854187817... × 10−12 F·m−1.

For applicable frequency ranges, the real relative permittivity can be modelled by

    Image,    (7.6-41)

while the conductivity in [S/m] may be modelled by

    Image,    (7.6-42)

with Image being the center frequency in Hz.

A selection of material properties from [19] is given below. Since the simplified models for conductivity and relative permittivity are only applicable for frequencies between 1 and 10 GHz, Figure 7.6.8-1 presents curves up to 100 GHz at least for very, medium dry and wet ground, cf. [20].

Table 7.6.8-1 Material properties [19]

Material class

Relative permittivity

Conductivity

Frequency range in [GHz]

Image

Image

Image

Image

Concrete

5.31

0

0.0326

0.8095

1-100

Brick

3.75

0

0.038

0

1-10

Plasterboard

2.94

0

0.0116

0.7076

1-100

Wood

1.99

0

0.0047

1.0718

0.001-100

Floorboard

3.66

0

0.0044

1.3515

50-100

Metal

1

0

107

0

1-100

Very dry ground

3

0

0.00015

2.52

1-10

Medium dry ground

15

−0.1

0.035

1.63

1-10

Wet ground

30

−0.4

0.15

1.30

1-10

 

Image

Figure 7.6.8-1: Relative permittivity and conductivity as a function of frequency [20]

7.6.9     Absolute time of arrival #

To support simulations in which absolute time of arrival is important, the propagation time delay due to the total path length is considered in step 11 of the fast fading model as follows. The impulse response in NLOS is determined using equation (7.6-43) instead of (7.5-27) and the impulse response in LOS is determined using equation (7.6-44) instead of (7.5-30), where c is the speed of light. Δτ is generated from a lognormal distribution with parameters according to Table 7.6.9-1. Δτ is generated independently for links between the same UT and different BS sites. For InF and InH scenarios, the excess delay in NLOS, τ, should further be upper bounded by 2L/c, where L is the largest dimension of the factory hall and office room, i.e. L = max(length, width, height). For UMi-Street Canyon, UMa, SMa, and RMa scenarios, the excess delay is not upper bounded.

    Hu,sNLOSτ,t=n=12i=13mRiHu,s,n,mNLOStδτ-τn,i-d3Dcτ+n=3NHu,s,nNLOStδτ-τn-d3Dcτ(7.6.-43)

    Hu,sLOSτ,t=1KR+1Hu,sNLOSτ,t+KRKR+1Hu,s,1LOStδτ-τ1-d3Dc.    (7.6-44)

where, in LOS case,

    Hu,sNLOSτ,t=n=12i=13mRiHu,s,n,mNLOStδτ-τn,i-d3Dc+n=3NHu,s,nNLOStδτ-τn-d3Dc    (7.6-44a)

Table 7.6.9-1: Parameters for the absolute time of arrival model in NLOS

Scenarios

InH

InF-SL, InF-DL

InF-SH, InF-DH

UMi

UMa

RMa

SMa

lgΔτ=log10Δτ1s

μlgΔτ

-8.6

-7.5

-7.5

-7.5

-7.4

-8.33

-7.702

σlgΔτ

0.1

0.4

0.4

0.5

0.2

0.26

0.4

Correlation distance in the horizontal plane [m]

10

6

11

15

50

50

50

 

7.6.10     Dual mobility #

To support simulations that involve dual Tx and Rx mobility or scatterer mobility, the Doppler frequency component in the channel coefficient generation in step 11 in clause 7.5 should be updated as follows.

For the LOS path, the Doppler frequency is given by

    vn,m=rrx,n,mTvrx+rtx,n,mTvtxλ0    (7.6-45)

where rrx, n, m and rtx, n, mare defined in (7.5-23) and (7.5-24) respectively, and

Image

Image

For all other paths, the Doppler frequency component is given by:

    vn,m=rrx,n,mTvrx+rtx,n,mTvtx+2αn,mDn,mλ0    (7.6-46)

where Dn,m is a random variable from -vscatt to vscatt, αn,m is a random variable of Bernoulli distribution with mean p, and vscatt is the maximum speed of the clutter. Parameter p determines the proportion of mobile scatterers and can thus be selected to appropriately model statistically larger number of mobile scatterers (higher p) or statistically smaller number of mobile scatterers (e.g. in case of a completely static environment: p=0 results in all scatteres having zero speed). A typical value of p is 0.2.

7.6.11     Sources of EM interference #

When the simulation includes sources of EM interference in industrial scenarios, the channel(s) between the interferer(s) and the BS or UT should be modeled using the appropriate sub-scenario(s), e.g. clutter-embedded or elevated Tx or Rx and low or high clutter density. Characterization of EM interference sources is out of the scope of this TR.

7.6.12     Embedded devices #

To simulate devices that are embedded inside machinery or enclosures, an additional loss parameter Lembedded is added to the path loss in step 12. The additional path loss parameter Lembedded to embedded devices is left as a simulation assumption, where it may be formulated e.g. as a value or range, or derived using a methodology similar to the building penetration loss in clause 7.4.3.1.

7.6.13     Near-field channel model #

The near-field channel model is to support the simulations that involve the impacts of the spherical wavefront from the perspective of antenna array.

To model the antenna element-wise channel parameters, in the Step 11 in Clause 7.5, the following updates are considered to generate the channel impulse response:

For the NLOS channel impulse response, determine the NLOS channel coefficient for the two strongest clusters, say n = 1 and 2, instead of the equation (7.5-28) and for the N – 2 weakest clusters, say n = 3, 4,…, N, using Hu,s,nNLOSt=m=1MHu,s,n,mNLOSt instead of equation (7.5-22).

-    To model the antenna element-wise phase at TRP side, the NLOS channel coefficient, Hu,s,n,mNLOSt, is given by:

Hu,s,n,mNLOSt=PnMFrx,u,θθn,m,ZOA,ϕn,m,AOAFrx,u,ϕθn,m,ZOA,ϕn,m,AOATexpjΦn,mθθkn,m-1expjΦn,mθϕkn,m-1expjΦn,mϕθexpjΦn,mϕϕFtx,s,θθn,m,ZOD,ϕn,m,AODFtx,s,ϕθn,m,ZOD,ϕn,m,AOD

    expj2πd1,n,m-d1,n,mrtx,n,m-dtx,sλ0expj2πrrx,n,mTdrx,uλ0expj2πrrx,n,mTvλ0t    (7.6-47)

where, the rtx,n,m is the spherical unit vector with azimuth departure angle and elevation departure angle for ray m of cluster n. dtx,s is the vector pointing from reference point to transmit antenna element s, wherein the reference point is the physical center of the antenna array/center at Tx side. The d1,n,m is the distance calculated as:

    d1,n,m=sTRP,n*d3Dτ*c+τn,i*c, for the two strongest clusters, say n = 1 and 2sTRP,n*d3Dτ*c+τn*c,for the N - 2 weakest clusters, say n = 3, 4,…, N,

w here the d3D refers to the 3D distance between reference point at TRP and UT side. The Δτ refers to the excess delay, which is only applicable when it’s not in LOS case, and generated according to the Clause 7.6.9, otherwise Δτ is assumed to be 0. The τn,i refers to the delay of i-th sub-cluster mapping to the rays defined in Table 7.5-5. The ray index, m, should be determined based on the sub-cluster information in Table 7.5-5. For the sTRP,n, if the nth cluster is one of the k1 strongest cluster, sTRP,n= 1, otherwise sTRP,n is generated according to the Betaα,β. The corresponding value of k1 and Beta distribution is defined in Table 7.6-13-1, respectively.

Table 7.6-13-1: Parameters for Uma, Umi, Indoor-Office and Indoor-Factory

Scenarios

UMa

UMi

InH

InF

k1

2

2

4

4

Beta distribution

α

1.93

1.53

1.25

1.38

β

1.33

1.42

1.27

1.26

 

Optionally,

-    To model the antenna element-wise phase at UT side additionally, the Hu,s,n,mNLOSt is given by:

Hu,s,n,mNLOSt=PnMFrx,u,θθn,m,ZOA,ϕn,m,AOAFrx,u,ϕθn,m,ZOA,ϕn,m,AOATexpjΦn,mθθkn,m-1expjΦn,mθϕkn,m-1expjΦn,mϕθexpjΦn,mϕϕFtx,s,θθn,m,ZOD,ϕn,m,AODFtx,s,ϕθn,m,ZOD,ϕn,m,AOD

    expj2πd2,n,m-d2,n,mrrx,n,m-drx,uλ0expj2πd1,n,m-d1,n,mrtx,n,m-dtx,sλ0expj2πrrx,n,mTvλ0t    (7.6-48)

where rrx,n,m is the spherical unit vector with azimuth arrival angle and elevation arrival angle for ray m of cluster n. drx,u is the vector pointing from reference point to receive antenna element u, wherein the reference point is the physical center of the antenna array/center at Rx side. The d2,n,m is the distance calculated as:

    d2,n,m=sUE,n*d3Dτ*c+τn,i*c, for the two strongest clusters, say n = 1 and 2sUE,n*d3Dτ*c+τn*c,for the N - 2 weakest clusters, say n = 3, 4,…, N,

w here sUE,n=1, if the nth cluster is one of the k1 strongest cluster, otherwise sUE,n =1-sTRP,n. Note that it is assumed that only single non-specular interaction along with specular reflection(s) is considered in the propagation channel and most of the sources of non-specular interaction are assumed to be a point scatterer or diffractions.

-    To model the antenna element-wise antenna field patterns additionally, the Hu,s,n,mNLOSt is given by:

Hu,s,n,mNLOSt=PnMFrx,u,θθn,m,ZOA,u,ϕn,m,AOA,uFrx,u,ϕθn,m,ZOA,u,ϕn,m,AOA,uTexpjΦn,mθθkn,m-1expjΦn,mθϕkn,m-1expjΦn,mϕθexpjΦn,mϕϕFtx,s,θθn,m,ZOD,s,ϕn,m,AOD,sFtx,s,ϕθn,m,ZOD,s,ϕn,m,AOD,s

    expj2πd2,n,m-d2,n,mrrx,n,m-drx,uλ0expj2πd1,n,m-d1,n,mrtx,n,m-dtx,sλ0expj2πrrx,n,mTvλ0t    (7.6-49)

where θn,m,ZOA,u and ϕn,m,AOA,u are the antenna element-wise elevation arrival angles and azimuth arrival angles, respectively, for ray m of cluster n for receive antenna element u, and θn,m,ZOD,s and ϕn,m,AOD,s are the antenna element-wise elevation departure angles and azimuth departure angles, respectively, for ray m of cluster n for transmit antenna element s.

In the LOS channel impulse response, determine the LOS channel coefficient as:

-    To model the antenna element-wise phase, the equation (7.5-29) in Clause 7.5 is replaced by:

Hu,s,1LOSt=Frx,u,θθLOS,ZOA,ϕLOS,AOAFrx,u,ϕθLOS,ZOA,ϕLOS,AOAT100-1Ftx,s,θθLOS,ZOD,ϕLOS,AODFtx,s,ϕθLOS,ZOD,ϕLOS,AOD

    exp-j2πd3Dλ0exp-j2πru,s-d3Dλ0expj2πrrx,LOSTvλ0t    (7.6-50)

where, the ru,s refers to the vector determined by the location of the uth antenna element at receiver and the sth antenna element at transmitter. The d3D refers to the 3D distance between reference point at TRP and UT side, wherein the the reference point is the physical center of the antenna array/center of the device.

Optionally,

-    To model the element-wise antenna field patterns additionally, the equation (7.5-29) in Clause 7.5 is replaced by:

Hu,s,1LOSt=Frx,u,θθLOS,ZOA,u,s,ϕLOS,AOA,u,sFrx,u,ϕθLOS,ZOA,u,s,ϕLOS,AOA,u,sT100-1Ftx,s,θθLOS,ZOD,u,s,ϕLOS,AOD,u,sFtx,s,ϕθLOS,ZOD,u,s,ϕLOS,AOD,u,s

    exp-j2πd3Dλ0exp-j2πru,s-d3Dλ0expj2πrrx,LOSTvλ0t    (7.6-51)

w here θLOS,ZOA,u,s, ϕLOS,AOA,u,s, θLOS,ZOD,u,s, ϕLOS,AOD,u,s are the respective antenna element-wise elevation arrival angles, azimuth arrival angles, elevation departure angles and azimuth departure angles of LOS path between the transmit antenna element s and receive antenna element u.

7.6.14     Spatial non-stationarity channel model #

7.6.14.1     Spatial non-stationarity channel model at TRP side #

7.6.14.1.1     Introduction #

The spatial non-stationarity (SNS) channel model is to support the simulations that involve the impacts of the antenna element-wise power variation at TRP. Two alternative models (i.e., Physical blocker-based Model and Stochastic based Model) are provided for the SNS modelling.

From the perspective of the simulation requirements:

-    The physical blocker-based model can be considered for simulation where physical accuracy and consistency are desired.

-    The stochastic based model can be considered for simulation where computational efficient SNS modelling is desired.

From the perspective of the model mechanism:

-    To reflect the SNS phenomenon due to partial blockage, the physical blocker-based model can be considered in simulation.

-    To reflect the SNS phenomenon due to incomplete scattering, the stochastic based model can be considered in simulation.

The stochastic based model is recommended to be used in the simulation except for the case where it is not applicable.

7.6.14.1.2     Physical blocker-based Model #

To model the antenna element-wise power variation at TRP side for each UT, the following additional steps between Step 9 and 10 in Clause 7.5 are introduced to calculate the antenna element-wise power variation, i.e., for NLOS cluster, LdB,u,n,mNLOS if the TRP is the receiver or LdB,s,n,mNLOS if the TRP is the transmitter, and LdB,u,s,1LOS for LOS ray.

Step I: Determine blockers

The number of blockers (K), their vertical and horizontal extensions (hkand wk), locations (xk,yk,zk) are determined according to ‘Step a’ in the Clause 7.6.4.2:

Note:

-    Recommended parameters for typical blockers are provided in Table 7.6.14.1.1-1.

-    The blocking effect diminishes with increasing distance to the blocker. For implementation purposes it may be sufficient to consider only the K nearest blockers from a BS.

Table 7.6.14.1.1-1: Blocker parameters

 

Typical set of blockers

Blocker dimensions

Mobility pattern

Indoor; Outdoor; InF

Human

Cartesian: w=0.3m; h=1.7m

Stationary or up to 3 km/h

Outdoor

Vehicle

Cartesian: w=4.8m; h=1.4m

Stationary or up to 100 km/h

InF

AGV

Cartesian: w=3m; h=1.5m

Up to 30 km/h

InF

Industrial robot

Cartesian: w=2m; h=0.2m

Up to 3 m/s

Outdoor

Billboard

Cartesian: w=2.4m; h=3.6m

Stationary

Outdoor

Street lamp

Cartesian: w=0.4m; h=0.8m

Stationary

Outdoor

Building edge

Cartesian: w=50 m; h=20 m

Stationary

Indoor

Pillar

Cartesian: w=0.3m; h=3m

Stationary

 

Step II: Determine the antenna element-wise power variation per ray for each element

The antenna element-wise power variation caused by each blocker to the mth ray of nth cluster for each element is modelled using a simple knife edge diffraction model and is given by:

    LdB,u,n,mNLOS or LdB,s,n,mNLOS or LdB,u,s,1LOS =-20log10(1-(Fh1+Fh2)(Fw1+Fw2)),-20log100.5-F, if blocker is building edge    (7.6-52)

where F represents one of Fh1, Fh2, Fw1, and Fw2 which account for knife edge diffraction at the four edges, and are given by equation (7.6-30) in Clause 7.6.4.2 by assuming that:

-     D1h1|h2|w1|w2D1h1h2w1|w2 are the projected (onto the side and top view planes) distances between the uth antenna element at receiver and four edges of the corresponding blocker, and D2h1|h2|w1|w2 D2h1h2w1|w2 are the projected (onto the side and top view planes) distances between the sth antenna element at transmitter and four edges of the corresponding blocker.

-    The side view plane is perpendicular to the horizontal ground plane. The top view is perpendicular to the side view.

-    For each cluster and each element at BS side, the blocker screen is rotated around its centre such that the arrival (if the BS is receiver)/or departure (if the BS is transmitter) direction of the corresponding path is always perpendicular to the screen. It should be noted that different rotations are required for each individual ray and each antenna element. Meanwhile, the base and top edges of the screens are always parallel to the horizontal plane. As the screen is perpendicular to each ray, r is the distance between the uth antenna element at receiver and the sth antenna element at transmitter for the direct path in LOS, and r' is the distance between the blocker screen and the uth antenna element at receiver or the sth antenna element at transmitter, projected onto the incoming ray direction, for all the other (NLOS) paths.

Step III: Apply the antenna element-wise power variation in coefficient generation

The antenna element-wise power variation is considered in the Step 11 for coefficient generation in Clause 7.5 with following:

-    For the NLOS cluster, the channel coefficients Hu,s,nNLOSt are calculated according to the equation (7.5-28) in Clause 7.5 by applying the LdB,u,n,mNLOS or LdB,s,n,mNLOS.

-    In the LOS case, the LOS channel coefficient Hu,s,1LOSt is calculated according to the equation (7.5-29) in Clause 7.5 by applying the LdB,u,s,1LOS for LOS path.

7.6.14.1.3     Stochastic based Model #

To model the antenna element-wise power variation at TRP side for each UT, the following additional steps between Step 9 and 10 in Clause 7.5 are introduced to calculate the antenna element-wise power attenuation factor, i.e., αu,n if the TRP is the receiver or αs,n if the TRP is the transmitter.

Step I: Determine the visibility limiting condition for the nth cluster of UT

For the nth cluster, the cluster is assumed to be impacted by the visibility limitation, if the xn<Prsns, where the xn is generated per cluster using:

    xn ~ unif(0,1).    (7.6-53)

The Prsns is generated per UT using the truncated normal distribution,

    Prsns=Nμ, σ2.    (7.6-54)

where 0≤Prsns≤1,which is defined in Table 7.6.14.1.2-1.

Table 7.6.14.1.2-1: Parameters for Prsns

 

RMa

SMa

UMa

UMi

InH

InF

μ

0.14

0.24

0.56

0.49

0.31

0.32

σ

0.08

0.07

0.20

0.18

0.08

0.06

 

Step II: Generate the visibility region (VR)

For the nth cluster which is assumed to be impacted by the visibility limitation, the visibility region is generated as a rectangle region with dimension a*b by using:

    a~unif(Vn*W,W),    (7.6-55)

    b=Vn*H*W/ a,    (7.6-56)

where H is the height of TRP antenna array (i.e., in vertical direction) and W is the width of antenna array (i.e., in horizontal direction). The Vn is generated as:

    Vn=A*e-maxPn-PnR+Β+δ.    (7.6-57)

where Pn refers to the power of cluster generated by (7.5-6) in dB scale. In the case of LOS condition, the LOS path is considered as an additional cluster, and the power ratio of the LOS path to NLOS clusters follows the Ricean K-factor generated in Clause 7.5. The parameters A, B, R, and δ are defined in Table 7.6.14.1.2-2.

Table 7.6.14.1.2-2: Parameters for A, B, R, and δ

Parameters

RMa

SMa

UMa

UMi

InH

InF

A

0.16

0.06

0.15

0.12

0

0

B

0.74

0.56

0.45

0.48

0.6

0.57

R

60

23

33

50

NA

NA

δ

Nμ, σ2

μ

0

0

0

0

0

0

σ2

0.0016

0.0013

0.0015

0.001

0.0011

0.002

Each visibility region is assumed to be randomly located at one of the corners of TRP antenna array (i.e., reference conrner), which is determined by the following conditions:

-    The probability of VR located at either left or right part of antenna array in horizontal domain is 0.5.

-    The probability of VR located at either upper or lower part of antenna array in vertical domain is 0.5.

Step III: Determine the antenna element-wise power attenuation factor

For the nth cluster which is assumed not to be impacted by the visibility limitation, the αu,n=1 or αs,n=1.

For the nth cluster which is assumed to be impacted by the visibility limitation, the αu,n=αi,n or αs,n=αi,n.

    αi,n=1 ,xi-x0,na,yi-y0,nbe-xi-xa,nDnC ,xi-x0,n>a,yi-y0,nb e-yi-yb,nDnC ,xi-x0,na,yi-y0,n>b e-xi-xa,n2+yi-yb,n2DnC ,others     (7.6-58)

where, Dn=xA,n-xa,n2+yB,n-yb,n2 , xA,n, yB,n is the coordinate of the other corner of the antenna array on the diagonal with reference corner x0,n, y0,n, and xa,n, yb,n is the coordinate of the other corner of the visibility region on the diagonal with reference corner x0,n, y0,n. The xi, yi denotes the coordinate of the antenna element i. C is the roll-off factor between the visible and invisible regions and defined in Table 7.6.14.1.2-3.

Table 7.6.14.1.2-3: Parameters for C

 

UMa/UMi/RMa/SMa/InF/InH

C

13

Step IV: Apply the antenna element-wise power attenuation factor in coefficient generation

The antenna element-wise power variation is considered in the Step 11 for coefficient generation in Clause 7.5 with following:

-    For the NLOS cluster, the channel coefficients Hu,s,nNLOSt are calculated according to the equation (7.5-28) in Clause 7.5 by applying the αu,n or αs,n.

-    In the LOS case, the LOS channel coefficient Hu,s,1LOSt is calculated according to the equation (7.5-29) in Clause 7.5 by applying the αu,1 or αs,1 for LOS path.

7.6.14.2     Spatial non-stationarity channel model at UT side #

The spatial non-stationarity (SNS) channel model is to support the simulations that involve the impacts of the antenna element-wise power variation at UT.

The ratio of UT with impact of SNS is 90% and for each UT, the probability of each usage scenario is defined in Table 7.6.14.2-1:

Table 7.6.14.2-1: The probability of each scenario

Usage scenario

One hand grip

Dual hand grip

Head and one hand grip

Probability

58%

29%

13%

 

The antenna element-wise power variation at UT side, i.e., LdB,uUT if the UT is receiver or LdB,sUT if the UT is transmitter, is defined in Table 7.6.14.2-2 for each candidate antenna location in Clause 7.3.

Table 7.6.14.2-2: The power attenuation for each candidate antenna location

Antenna index

Power attenuation (dB)

One hand grip

Dual hand grip

Head and one hand grip

Below 1 GHz

1 - 8.4 GHz

14.5 - 15.5 GHz

 

Below 1 GHz

1 - 8.4 GHz

14.5 - 15.5 GHz

 

Below 1 GHz

1 - 8.4 GHz

14.5 - 15.5 GHz

1

-

0.7

1.0

-

11.0

3.8

-

3.7

4.0

2

-

4.1

 2.4

-

1.1

 3.8

-

4.6

3.7

3

-

3.3

3.8

-

10.5

3.8

-

4.3

3.3

4

13.6

7.2

3.6

5.8

5.6

 2.6

15.1

7.8

3.3

5

-

10.8

3.8

-

1.5

1.0

-

11.7

3.3

6

-

9.1

 2.4

-

1.4

 1.0

-

10.1

3.7

7

-

0.7

1.0

-

1.3

1.0

-

2.9

4.0

8

2.4

0.6

1.0

5.6

6.2

 2.6

4.9

4.2

4.0

NOTE 1:     For below 1GHz, the model is only applicable for 2-antenna system with antenna location index 4 and 8.

NOTE 2:     The selection of number of antenna and antenna index will be determined as part of the assumption for simulation.

 

The antenna element-wise power variation is applied in the Step 11 for coefficient generation in Clause 7.5.

7.6.15     Number of cluster variability model #

The number of clusters observed in real-world deployments can vary due to one or more factors, including but not limited to the propagation environment, carrier frequency, bandwidth, and spatial resolution. To capture this variability in simulations, the number of clusters for each link can be chosen between a closed range of [D1, D2]. The values of D1 and D2 are provided in Table 7.6.15-1. The selection of the number of clusters for each link is determined by the user of the model. The selected number of clusters for each link replaces the number of clusters N in Table 7.5.6-Part 1 and 7.5.6-Part 2. A similar framework for modeling the variability in number of clusters is also adopted in the METIS channel model [6].

Table 7.6.15-1: Range of number of clusters

Scenarios

LOS

NLOS

O2I

UMi-Street Canyon

D1: 6

D2: 12

D1: 6

D2: 19

D1: 6

D2: 12

UMa

D1: 10

D2: 12

D1: 15

D2: 20

D1: 10

D2: 12

Indoor-office

D1: 7

D2: 15

D1: 6

D2: 19

N/A

 

7.6.16     Polarization power variability model #

To support simulation of polarization power variability, the polarization variability powers ηθθ, ηθϕ, ηϕθ and ηϕϕ for each ray m of each cluster n is generated and applied to NLOS channel coefficients. The equation (7.5-22) and equation (7.5-28) are replaced by equation (7.6-59) and (7.6-60), respectively, to enable polarization power variability.

Hu,s,nNLOSt=PnMm=1MFrx,u,θθn,m,ZOA,ϕn,m,AOAFrx,u,ϕθn,m,ZOA,ϕn,m,AOATηn,m.θθexpjΦn,mθθηn,m,θϕκn,m-1expjΦn,mθϕηn,m,ϕθκn,m-1expjΦn,mϕθηn,m,ϕϕexpjΦn,mϕϕ

Ftx,s,θθn,m,ZOD,ϕn,m,AODFtx,s,ϕθn,m,ZOD,ϕn,m,AODexpj2πrrx,n,mTdrx,uλ0expj2πrtx,n,mTdtx,sλ0expj2πrrx,n,mTvλ0t,    (7.6-59)

Hu,s,,n,mNLOSt=PnMFrx,u,θθn,m,ZOA,ϕn,m,AOAFrx,u,ϕθn,m,ZOA,ϕn,m,AOATηn,m.θθexpjΦn,mθθηn,m,θϕκn,m-1expjΦn,mθϕηn,m,ϕθκn,m-1expjΦn,mϕθηn,m,ϕϕexpjΦn,mϕϕ

Ftx,s,θθn,m,ZOD,ϕn,m,AODFtx,s,ϕθn,m,ZOD,ϕn,m,AODexpj2πrrx,n,mTdrx,uλ0expj2πrtx,n,mTdtx,sλ0expj2πrrx,n,mTvλ0t,    (7.6-60)

where Qn,m,θθ,θϕ,ϕθ,ϕϕ is a Normal distributed random variable with mean 0 and variance 32, N0,32, independently drawn for each ray, cluster, and polarization component, and used to generate polarization variability powers ηθθ, ηθϕ, ηϕθ and ηϕϕ based on following equations:

    η'n,m,θθ,θϕ,ϕθ,ϕϕ=10Qn,m,θθ,θϕ,ϕθ,ϕϕ/10,    (7.6-61)

    ηn,m,θθ,θϕ,ϕθ,ϕϕ=η'n,m,θθ,θϕ,ϕθ,ϕϕ2+2κn,m-1η'n,m,θθ+η'n,m,ϕϕ+η'n,m,θϕκn,m-1+η'n,m,ϕθκn,m-1.    (7.6-62)

NOTE:    Polarization power variability model is at least applicable for Model-2 of the antenna polarization modelling.

 

7.7.1     Clustered Delay Line (CDL) models #

The CDL models are defined for the full frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz. CDL models can be implemented by e.g., coefficient generation Step 10 and Step 11 in Clause 7.5 or generating TDL model using spatial filter from Clause 7.7.4. Three CDL models, namely CDL-A, CDL-B and CDL-C are constructed to represent three different channel profiles for NLOS while CDL-D and CDL-E are constructed for LOS, the parameters of which can be found respectively in Table 7.7.1-1, Table 7.7.1-2 , Table 7.7.1-3, Table 7.7.1-4 and Table 7.7.1-5.

Each CDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the procedure described in Clause 7.7.3. Each CDL model can also be scaled in angles so that the model achieves desired angle spreads, according to the procedure described in Clause 7.7.5.1.

For LOS channel models, the K-factor of CDL-D and CDL-E can be set to a desired value following the procedure described in Clause 7.7.6.

For modelling effect of beamforming in a simplified way, a brick-wall window can be applied to a delay-scaled CDL model. The power shall be normalized after applying the window. A TDL model for simplified evaluations can be obtained from the CDL model, according to this method.

The following step by step procedure should be used to generate channel coefficients using the CDL models.

 

Step 1: Generate departure and arrival angles

Generate arrival angles of azimuth using the following equation

    Image,    (7.7-0a)

Where φn,AOA is the cluster AOA and cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Tables 7.7.1.1 – 7.7.1.5 below, and αm denotes the ray offset angles within a cluster given by Table 7.5-3. If angular scaling according to Clause 7.7.5.1 is used, this is applied to the ray angles Image, The generation of AOD (Image), ZSA (Image), and ZSD (Image) follows a procedure similar to AOA as described above.

 

Step 2: Coupling of rays within a cluster for both azimuth and elevation

Couple randomly AOD angles Image to AOA angles Image within a cluster n. Couple randomly ZOD angles Image with ZOA angles Imageusing the same procedure. Couple randomly AOD angles Image with ZOD angles Imagewithin a cluster n.

 

Step 3: Generate the cross polarization power ratios

Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n as

    Image,    (7.7-0b)

where X is the per-cluster XPR in dB from Tables 7.7.1.1 – 7.7.1.5.

 

Step 4: Coefficient generation

Follow the same procedure as in Steps 10 and 11 in Clause 7.5, with the exception that all clusters are treated as "weaker cluster", i.e. no further sub-clusters in delay should be generated. Additional clusters representing delay spread of the stronger clusters are already provided in Tables 7.7.1.1 – 7.7.1.5.

 

Table 7.7.1-1. CDL-A

Cluster #

Normalized delay

Power in [dB]

AOD in [°]

AOA in [°]

ZOD in [°]

ZOA in [°]

1

0.0000

-13.4

-178.1

51.3

50.2

125.4

2

0.3819

0

-4.2

-152.7

93.2

91.3

3

0.4025

-2.2

-4.2

-152.7

93.2

91.3

4

0.5868

-4

-4.2

-152.7

93.2

91.3

5

0.4610

-6

90.2

76.6

122

94

6

0.5375

-8.2

90.2

76.6

122

94

7

0.6708

-9.9

90.2

76.6

122

94

8

0.5750

-10.5

121.5

-1.8

150.2

47.1

9

0.7618

-7.5

-81.7

-41.9

55.2

56

10

1.5375

-15.9

158.4

94.2

26.4

30.1

11

1.8978

-6.6

-83

51.9

126.4

58.8

12

2.2242

-16.7

134.8

-115.9

171.6

26

13

2.1718

-12.4

-153

26.6

151.4

49.2

14

2.4942

-15.2

-172

76.6

157.2

143.1

15

2.5119

-10.8

-129.9

-7

47.2

117.4

16

3.0582

-11.3

-136

-23

40.4

122.7

17

4.0810

-12.7

165.4

-47.2

43.3

123.2

18

4.4579

-16.2

148.4

110.4

161.8

32.6

19

4.5695

-18.3

132.7

144.5

10.8

27.2

20

4.7966

-18.9

-118.6

155.3

16.7

15.2

21

5.0066

-16.6

-154.1

102

171.7

146

22

5.3043

-19.9

126.5

-151.8

22.7

150.7

23

9.6586

-29.7

-56.2

55.2

144.9

156.1

Per-Cluster Parameters

Parameter

cASD in [°]

cASA in [°]

cZSD in [°]

cZSA in [°]

XPR in [dB]

Value

5

11

3

3

10

 

Table 7.7.1-2. CDL-B

Cluster #

Normalized delay

Power in [dB]

AOD in [°]

AOA in [°]

ZOD in [°]

ZOA in [°]

1

0.0000

0

9.3

-173.3

105.8

78.9

2

0.1072

-2.2

9.3

-173.3

105.8

78.9

3

0.2155

-4

9.3

-173.3

105.8

78.9

4

0.2095

-3.2

-34.1

125.5

115.3

63.3

5

0.2870

-9.8

-65.4

-88.0

119.3

59.9

6

0.2986

-1.2

-11.4

155.1

103.2

67.5

7

0.3752

-3.4

-11.4

155.1

103.2

67.5

8

0.5055

-5.2

-11.4

155.1

103.2

67.5

9

0.3681

-7.6

-67.2

-89.8

118.2

82.6

10

0.3697

-3

52.5

132.1

102.0

66.3

11

0.5700

-8.9

-72

-83.6

100.4

61.6

12

0.5283

-9

74.3

95.3

98.3

58.0

13

1.1021

-4.8

-52.2

103.7

103.4

78.2

14

1.2756

-5.7

-50.5

-87.8

102.5

82.0

15

1.5474

-7.5

61.4

-92.5

101.4

62.4

16

1.7842

-1.9

30.6

-139.1

103.0

78.0

17

2.0169

-7.6

-72.5

-90.6

100.0

60.9

18

2.8294

-12.2

-90.6

58.6

115.2

82.9

19

3.0219

-9.8

-77.6

-79.0

100.5

60.8

20

3.6187

-11.4

-82.6

65.8

119.6

57.3

21

4.1067

-14.9

-103.6

52.7

118.7

59.9

22

4.2790

-9.2

75.6

88.7

117.8

60.1

23

4.7834

-11.3

-77.6

-60.4

115.7

62.3

Per-Cluster Parameters

Parameter

cASD in [°]

cASA in [°]

cZSD in [°]

cZSA in [°]

XPR in [dB]

Value

10

22

3

7

8

 

Table 7.7.1-3. CDL-C

Cluster #

Normalized delay

Power in [dB]

AOD in [°]

AOA in [°]

ZOD in [°]

ZOA in [°]

1

0

-4.4

-46.6

-101

97.2

87.6

2

0.2099

-1.2

-22.8

120

98.6

72.1

3

0.2219

-3.5

-22.8

120

98.6

72.1

4

0.2329

-5.2

-22.8

120

98.6

72.1

5

0.2176

-2.5

-40.7

-127.5

100.6

70.1

6

0.6366

0

0.3

170.4

99.2

75.3

7

0.6448

-2.2

0.3

170.4

99.2

75.3

8

0.6560

-3.9

0.3

170.4

99.2

75.3

9

0.6584

-7.4

73.1

55.4

105.2

67.4

10

0.7935

-7.1

-64.5

66.5

95.3

63.8

11

0.8213

-10.7

80.2

-48.1

106.1

71.4

12

0.9336

-11.1

-97.1

46.9

93.5

60.5

13

1.2285

-5.1

-55.3

68.1

103.7

90.6

14

1.3083

-6.8

-64.3

-68.7

104.2

60.1

15

2.1704

-8.7

-78.5

81.5

93.0

61.0

16

2.7105

-13.2

102.7

30.7

104.2

100.7

17

4.2589

-13.9

99.2

-16.4

94.9

62.3

18

4.6003

-13.9

88.8

3.8

93.1

66.7

19

5.4902

-15.8

-101.9

-13.7

92.2

52.9

20

5.6077

-17.1

92.2

9.7

106.7

61.8

21

6.3065

-16

93.3

5.6

93.0

51.9

22

6.6374

-15.7

106.6

0.7

92.9

61.7

23

7.0427

-21.6

119.5

-21.9

105.2

58

24

8.6523

-22.8

-123.8

33.6

107.8

57

Per-Cluster Parameters

Parameter

cASD in [°]

cASA in [°]

cZSD in [°]

cZSA in [°]

XPR in [dB]

Value

2

15

3

7

7

 

Table 7.7.1-4. CDL-D.

Cluster #

Cluster PAS

Normalized Delay

Power in [dB]

AOD in [°]

AOA in [°]

ZOD in [°]

ZOA in [°]

1

Specular(LOS path)

0

-0.2

0

-180

98.5

81.5

Laplacian

0

-13.5

0

-180

98.5

81.5

2

Laplacian

0.035

-18.8

89.2

89.2

85.5

86.9

3

Laplacian

0.612

-21

89.2

89.2

85.5

86.9

4

Laplacian

1.363

-22.8

89.2

89.2

85.5

86.9

5

Laplacian

1.405

-17.9

13

163

97.5

79.4

6

Laplacian

1.804

-20.1

13

163

97.5

79.4

7

Laplacian

2.596

-21.9

13

163

97.5

79.4

8

Laplacian

1.775

-22.9

34.6

-137

98.5

78.2

9

Laplacian

4.042

-27.8

-64.5

74.5

88.4

73.6

10

Laplacian

7.937

-23.6

-32.9

127.7

91.3

78.3

11

Laplacian

9.424

-24.8

52.6

-119.6

103.8

87

12

Laplacian

9.708

-30.0

-132.1

-9.1

80.3

70.6

13

Laplacian

12.525

-27.7

77.2

-83.8

86.5

72.9

Per-Cluster Parameters

Parameter

cASD in [°]

cASA in [°]

cZSD in [°]

cZSA in [°]

XPR in [dB]

Value

5

8

3

3

11

 

Table 7.7.1-5. CDL-E.

Cluster #

Cluster PAS

Normalized Delay

Power in [dB]

AOD in [°]

AOA in [°]

ZOD in [°]

ZOA in [°]

1

Specular (LOS path)

0.000

-0.03

0

-180

99.6

80.4

Laplacian

0.000

-22.03

0

-180

99.6

80.4

2

Laplacian

0.5133

-15.8

57.5

18.2

104.2

80.4

3

Laplacian

0.5440

-18.1

57.5

18.2

104.2

80.4

4

Laplacian

0.5630

-19.8

57.5

18.2

104.2

80.4

5

Laplacian

0.5440

-22.9

-20.1

101.8

99.4

80.8

6

Laplacian

0.7112

-22.4

16.2

112.9

100.8

86.3

7

Laplacian

1.9092

-18.6

9.3

-155.5

98.8

82.7

8

Laplacian

1.9293

-20.8

9.3

-155.5

98.8

82.7

9

Laplacian

1.9589

-22.6

9.3

-155.5

98.8

82.7

10

Laplacian

2.6426

-22.3

19

-143.3

100.8

82.9

11

Laplacian

3.7136

-25.6

32.7

-94.7

96.4

88

12

Laplacian

5.4524

-20.2

0.5

147

98.9

81

13

Laplacian

12.0034

-29.8

55.9

-36.2

95.6

88.6

14

Laplacian

20.6419

-29.2

57.6

-26

104.6

78.3

Per-Cluster Parameters

Parameter

cASD in [°]

cASA in [°]

cZSD in [°]

cZSA in [°]

XPR in [dB]

Value

5

11

3

7

8

 

7. 7.2    Tapped Delay Line (TDL) models #

The TDL models for simplified evaluations, e.g., for non-MIMO evaluations, are defined for the full frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz.

Three TDL models, namely TDL-A, TDL-B and TDL-C, are constructed to represent three different channel profiles for NLOS while TDL-D and TDL-E are constructed for LOS, the parameters of which can be found respectively in Table 7.7.2-1, Table 7.7.2-2 , Table 7.7.2-3, Table 7.7.2-4 and Table 7.7.2-5.

The Doppler spectrum for each tap is characterized by a classical (Jakes) spectrum shape and a maximum Doppler shift fD where Image. Due to the presence of a LOS path, the first tap in TDL-D and TDL-E follows a Ricean fading distribution. For those taps the Doppler spectrum additionally contains a peak at the Doppler shift fS = 0.7 fD with an amplitude such that the resulting fading distribution has the specified K-factor.

Each TDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the procedure described in Clause 7.7.3.

For LOS channel models, the K-factor of TDL-D and TDL-E can be set to a desired value following the procedure described in Clause 7.7.6.

Table 7.7.2-1. TDL-A

Tap #

Normalized delay

Power in [dB]

Fading distribution

1

0.0000

-13.4

Rayleigh

2

0.3819

0

Rayleigh

3

0.4025

-2.2

Rayleigh

4

0.5868

-4

Rayleigh

5

0.4610

-6

Rayleigh

6

0.5375

-8.2

Rayleigh

7

0.6708

-9.9

Rayleigh

8

0.5750

-10.5

Rayleigh

9

0.7618

-7.5

Rayleigh

10

1.5375

-15.9

Rayleigh

11

1.8978

-6.6

Rayleigh

12

2.2242

-16.7

Rayleigh

13

2.1718

-12.4

Rayleigh

14

2.4942

-15.2

Rayleigh

15

2.5119

-10.8

Rayleigh

16

3.0582

-11.3

Rayleigh

17

4.0810

-12.7

Rayleigh

18

4.4579

-16.2

Rayleigh

19

4.5695

-18.3

Rayleigh

20

4.7966

-18.9

Rayleigh

21

5.0066

-16.6

Rayleigh

22

5.3043

-19.9

Rayleigh

23

9.6586

-29.7

Rayleigh

 

Table 7.7.2-2. TDL-B

Tap #

Normalized delay

Power in [dB]

Fading distribution

1

0.0000

0

Rayleigh

2

0.1072

-2.2

Rayleigh

3

0.2155

-4

Rayleigh

4

0.2095

-3.2

Rayleigh

5

0.2870

-9.8

Rayleigh

6

0.2986

-1.2

Rayleigh

7

0.3752

-3.4

Rayleigh

8

0.5055

-5.2

Rayleigh

9

0.3681

-7.6

Rayleigh

10

0.3697

-3

Rayleigh

11

0.5700

-8.9

Rayleigh

12

0.5283

-9

Rayleigh

13

1.1021

-4.8

Rayleigh

14

1.2756

-5.7

Rayleigh

15

1.5474

-7.5

Rayleigh

16

1.7842

-1.9

Rayleigh

17

2.0169

-7.6

Rayleigh

18

2.8294

-12.2

Rayleigh

19

3.0219

-9.8

Rayleigh

20

3.6187

-11.4

Rayleigh

21

4.1067

-14.9

Rayleigh

22

4.2790

-9.2

Rayleigh

23

4.7834

-11.3

Rayleigh

 

Table 7.7.2-3. TDL-C

Tap #

Normalized delays

Power in [dB]

Fading distribution

1

0

-4.4

Rayleigh

2

0.2099

-1.2

Rayleigh

3

0.2219

-3.5

Rayleigh

4

0.2329

-5.2

Rayleigh

5

0.2176

-2.5

Rayleigh

6

0.6366

0

Rayleigh

7

0.6448

-2.2

Rayleigh

8

0.6560

-3.9

Rayleigh

9

0.6584

-7.4

Rayleigh

10

0.7935

-7.1

Rayleigh

11

0.8213

-10.7

Rayleigh

12

0.9336

-11.1

Rayleigh

13

1.2285

-5.1

Rayleigh

14

1.3083

-6.8

Rayleigh

15

2.1704

-8.7

Rayleigh

16

2.7105

-13.2

Rayleigh

17

4.2589

-13.9

Rayleigh

18

4.6003

-13.9

Rayleigh

19

5.4902

-15.8

Rayleigh

20

5.6077

-17.1

Rayleigh

21

6.3065

-16

Rayleigh

22

6.6374

-15.7

Rayleigh

23

7.0427

-21.6

Rayleigh

24

8.6523

-22.8

Rayleigh

 

Table 7.7.2-4. TDL-D.

Tap #

Normalized delay

Power in [dB]

Fading distribution

1

0

-0.2

LOS path

0

-13.5

Rayleigh

2

0.035

-18.8

Rayleigh

3

0.612

-21

Rayleigh

4

1.363

-22.8

Rayleigh

5

1.405

-17.9

Rayleigh

6

1.804

-20.1

Rayleigh

7

2.596

-21.9

Rayleigh

8

1.775

-22.9

Rayleigh

9

4.042

-27.8

Rayleigh

10

7.937

-23.6

Rayleigh

11

9.424

-24.8

Rayleigh

12

9.708

-30.0

Rayleigh

13

12.525

-27.7

Rayleigh

NOTE:    The first tap follows a Ricean distribution with a K-factor of K1 = 13.3 dB and a mean power of 0dB.

 

Table 7.7.2-5. TDL-E.

Tap #

Normalized delay

Power in [dB]

Fading distribution

1

0

-0.03

LOS path

0

-22.03

Rayleigh

2

0.5133

-15.8

Rayleigh

3

0.5440

-18.1

Rayleigh

4

0.5630

-19.8

Rayleigh

5

0.5440

-22.9

Rayleigh

6

0.7112

-22.4

Rayleigh

7

1.9092

-18.6

Rayleigh

8

1.9293

-20.8

Rayleigh

9

1.9589

-22.6

Rayleigh

10

2.6426

-22.3

Rayleigh

11

3.7136

-25.6

Rayleigh

12

5.4524

-20.2

Rayleigh

13

12.0034

-29.8

Rayleigh

14

20.6519

-29.2

Rayleigh

NOTE:    The first tap follows a Ricean distribution with a K-factor of K1 = 22 dB and a mean power of 0dB.

 

7. 7.3    Scaling of delays #

The RMS delay spread values of both CDL and TDL models are normalized and they can be scaled in delay so that a desired RMS delay spread can be achieved. The scaled delays can be obtained according to the following equation:

    Image    (7.7-1)

in which

Image    is the normalized delay value of the nth cluster in a CDL or a TDL model    

Image    is the new delay value (in [ns]) of the nth cluster

Image    is the wanted delay spread (in [ns])

The example scaling parameters are selected according to Table 7.7.3-1 where the values have been chosen such that the RMS delay spreads span the range observed in measurements corresponding to the typical 5G evaluation scenarios. For information purposes, examples of such RMS delay spreads for the different scenarios are given in Table 7.7.3-2 where the "short-delay profile" corresponds to the median RMS delay spread for LOS scenarios, while "normal-delay profile" and "long-delay profile" correspond to the median and the 90th percentile RMS delay spread for NLOS scenarios according to the channel parameters in Table 7.5-6. It can therefore be understood that a particular RMS delay spread in Table 7.7.3-1 may occur in any scenario; however certain values may be more likely in some scenarios than in others.

The example parameters given in Table 7.7.3-1 does not preclude the use of other scaling values if this is found appropriate, for instance if additional scenarios are introduced or if e.g. the effect of beamforming needs to be captured in a TDL. Both of these examples can potentially result in an increased range of experienced RMS delay spreads.

Table 7.7.3-1. Example scaling parameters for CDL and TDL models.

Model

Image

Very short delay spread

10 ns

Short delay spread

30 ns

Nominal delay spread

100 ns

Long delay spread

300 ns

Very long delay spread

1000 ns

 

Table 7.7.3-2. Scenario specific scaling factors - for information only

Proposed Scaling Factor Image in [ns]

Frequency [GHz]

2

6

15

28

39

60

70

Indoor office

Short-delay profile

20

16

16

16

16

16

16

Normal-delay profile

39

30

24

20

18

16

16

Long-delay profile

59

53

47

43

41

38

37

UMi Street-canyon

Short-delay profile

65

45

37

32

30

27

26

Normal-delay profile

129

93

76

66

61

55

53

Long-delay profile

634

316

307

301

297

293

291

UMa

Short-delay profile

93

93

85

80

78

75

74

Normal-delay profile

363

363

302

266

249

228

221

Long-delay profile

1148

1148

955

841

786

720

698

RMa & RMa O2I

Short-delay profile

32

32

N/A

N/A

N/A

N/A

N/A

Normal-delay profile

37

37

N/A

N/A

N/A

N/A

N/A

Long-delay profile

153

153

N/A

N/A

N/A

N/A

N/A

UMi / UMa O2I

Normal-delay profile

240

Long-delay profile

616

 

7.7.4     Spatial filter for generating TDL channel model #

The TDL models described in Clause 7.7.2 are generated from the CDL models assuming ideal isotropic antennas at both Tx and Rx. It is also possible to generate TDL models by assuming non-isotropic antennas like directive horn antennas or array antennas.

The basic idea to generate a TDL model based on a filtered CDL model is shown in Figure 7.7.4-1 below.

Image

Figure 7.7.4-1 The basic idea for filtering the CDL model to TDL model.

7.7.4.1     Exemplary filters/antenna patterns #

Note that any filter/pattern can be applied on a CDL to derive a TDL for evaluating directional algorithms.

Example 1: Isotropic pattern

    Image    (7.7-2)

Example 2: Rectangular mask

    Image    (7.7-3)

with Image denotes beamwidth.

Example 3: Simplified antenna pattern given in [ITU-R M.2135]

Image

Figure 7.7.4-2 Simplified antenna pattern [ITU-R M.2135].

7.7.4.2     Generation procedure #

1.    The following steps are needed to generate tapped delay line (TDL) models:Choose a CDL model (e.g. CDL-A). Note that the models may be scaled according to Clause 7.7.5.1 prior to the filtering in order to represent different angular spreads.

2.    Choose spatial filters Image and Image defined in LCS

3.    Transform the spatial filter into GCS to obtain Image and Image such that the pointing direction Image is centered within the filter. The pointing direction may be defined

a.    by the dominant path Image with Image, where Image denotes the CDL cluster power values

b.    Or an arbitrary direction

4.    Calculate TDL cluster power values Image given the following equation

    Image    (7.7-4)

 

7. 7.5    Extension for MIMO simulations #

Extended MIMO link-level channel models can be constructed according to two alternative methods described in the following.

7. 7.5.1    CDL extension: Scaling of angles #

The angle values of CDL models are fixed, which is not very suitable for MIMO simulations for several reasons; The PMI statistics can become biased, and a fixed precoder may perform better than open-loop and on par with closed-loop or reciprocity beamforming. Furthermore, a CDL only represents a single channel realization. The predefined angle values in the CDL models can be generalized by introducing angular translation and scaling. By translation, mean angle μϕ,model can be changed to μϕ,desired and angular spread ASmodel can be changed to match ASdesired by scaling. The translated and scaled ray angles can be obtained according to the following equation:

    ϕn,m,scaled=ϕn,scaled+c{ASA, ASD,ZSA,ZSD}αm    (7.7-5)

    ϕLOS,scaled=ϕLOS,intermediate-μϕ,intermediate+μϕ,desired    (7.7-5a)

    ϕn,intermediate=s∙WrapTo180ϕn,model-μϕ,model    (7.7-5b)

    ϕLOS,intermediate=s∙WrapTo180ϕLOS,model-μϕ,model    (7.7-5c)

    ϕn,scaled=ϕn,intermediate-μϕ,intermediate+μϕ,desired    (7.7-5d)

in which:

-    μϕ,model is the cluster mean angle calculated using Annex A.4 with ϕn,model and ϕLOS,model as the input cluster angles,

-    μϕ,intermediate is the intermediate cluster mean angle calculated using Annex A.4 with ϕn,intermediate and ϕLOS,intermediate as input cluster angles,

-    s is a scale factor chosen to change the distribution of the angles based on calculation in Annex A.5. Table 7.7.5.1-1 shows required scale factor for typical desired angular spread values for AOD, AOD, ZOA, and ZOD,

-    ϕn,model    is the tabulated CDL cluster angle,

-    ASmodel    is the rms cluster angular spread of the tabulated CDL including the offset ray angles, calculated using the angular spread definition in Annex A.3,

-    μϕ,desired    is the desired cluster mean angle,

-    ASdesired    is the desired rms cluster angular spread,

-    ϕn,scaled    is the scaled CDL cluster angle of the nth cluster,

-    ϕn,m,scaled is the resulting scaled ray angle that corresponds to mth subpath angle of nth cluster,

-    cASA, ASD,ZSA,ZSD is the cluster-wise RMS angular spread of either custer ASA, ASD, ZSA, or ZSD from Table 7.7.1-1, 7.7.1-2, 7.7.1-3, 7.7.1-4, and 7.7.1-5,

-    αm is the offset angles from Table 7.5-3,

-    WrapTo180 is a function which wraps an azimuth angle to the half-open interval (-180, 180].

Table 7.7.5.1-1: Scale factor values for each CDL model

CDL Type

Desired AOD Spread°

Scale Factor (AOD)

Desired AOA Spread°

Scale Factor (AOA)

Desired ZOA Spread°

Scale Factor (ZOA)

Desired ZOD Spread°

Scale Factor (ZOD)

CDL-A

5

0.0680

30

0.3531

5

0.2397

1

0.0352

10

0.1360

45

0.5268

10

0.4802

3

0.1056

15

0.2041

60

0.6981

15

0.7225

5

0.1761

25

0.3405

 

 

 

 

 

 

CDL-B

5

0.1238

30

0.5417

5

0.6519

1

0.1940

10

0.2475

45

0.8081

10

1.3018

3

0.5822

15

0.3710

60

1.0709

15

1.9480

5

0.9705

25

0.6168

 

 

 

 

 

 

CDL-C

5

0.1281

30

0.4307

5

0.6476

1

0.3643

10

0.2568

45

0.6447

10

1.2971

3

1.0929

15

0.3864

60

0.8585

15

1.9504

5

1.8219

25

0.6513

 

 

 

 

 

 

CDL-D

5

0.3231

30

9.8888

5

4.3268

1

0.4477

10

0.6652

45

N/A

10

8.8868

3

1.3469

15

1.0594

60

N/A

15

14.0344

5

2.2579

25

5.8637

 

 

 

 

 

 

CDL-E

5

0.3950

30

2.9733

5

6.9195

1

0.9714

10

0.8009

45

N/A

10

14.8378

3

2.9180

15

1.2330

60

N/A

15

27.2849

5

4.8774

25

2.3627

 

 

 

0.2397

 

 

NOTE:     Values of Table 7.7.5.1-1 were computed based on scaling factor calculation method described in Annex A.5.

 

Alternatively, for CDL-D and CDL-E, if maintaining the LOS angle is desired, the translated and scaled ray angles can be obtained according to the following equation instead of using equation (7.7-5) and (7.7-5a):

    ϕn,m,scaled=ϕn,scaled+c{ASA, ASD,ZSA,ZSD}αm    (7.7-6)

    ϕLOS,scaled=ϕLOS,model    (7.7-6a)

    ϕn,scaled=ϕn,intermediate-ϕLOS,intermediate+ϕLOS,model    (7.7-6b)

in which:

-    ϕLOS,model is the LOS path angle of the model,

-    ϕn,scaled    is the scaled CDL cluster angle of the nth cluster.

The angular scaling is applied on the cluster angles including offsets from the tabulated cluster angles. Typical angular spreads for different scenarios can be obtained from the system-level model.

Example scaling values are:

-    AOD spread (ASD) for each CDL model: {5, 10, 15, 25} degrees.

-    AOA spread (ASA) for each CDL model: {30, 45, 60} degrees.

-    ZOA spread (ZSA) for each CDL model: {5, 10, 15} degrees.

-    ZOD spread (ZSD) for each CDL model: {1, 3, 5} degrees.

The angular scaling and translation can be applied to some or all of the azimuth and zenith angles of departure and arrival.

Note: The azimuth angles may need to be wrapped around to be within [0, 360] degrees, while the zenith angles may need to be clipped to be within [0, 180] degrees.

7. 7.5.2    TDL extension: Applying a correlation matrix #

The TDLs and the spatial-filtered TDLs can be used with the correlation matrices for MIMO link-level simulations. Typical correlation parameters can be derived from 1) delay & angular scaled CDLs with antenna array assumptions, 2) system-level model with antenna array assumptions, or 3) by selecting extreme cases, e.g. uncorrelated, highly correlated etc. For example, these following options can be considered:

1)    Zero correlation (IID channel coefficients) can be used for any number of antenna elements

2)    The correlation matrix construction method from TS36.101/104 [15][16] can be used for linear and planar (single- or dual-polarized) arrays.

-    Other correlation parameters α, β, γ than those specified in TS36.101/104 [15][16] and extensions to larger antenna arrays can be considered. For typical scenarios, α and β will be in the range 0-1

-    A representative set of values is {0,0.7,0.9,0.99}

Note:    This approach can be applied to TDLs derived from spatially filtered CDLs to emulate hybrid BF system

Note:    Other methodologies could also be developed, e.g.

-    extending the TS36.101/104 [15][16] procedure to planar arrays or more elements

-    using CDLs in combination with array assumptions to derive per-tap correlation matrices as in [17].

-    using the system-level model in combination with array assumptions to derive per-tap or per-channel correlation matrices

7. 7.6    K-factor for LOS channel models #

For the LOS channel models of CDL/TDL-D and CDL/TDL-E, the K-factor values may be changed by the user. Mean and standard deviation of K-factor values can be found in Table 7.5-6, although other values may also be used.

If the K-factor of a model shall be changed to Image [dB], the cluster powers for the Laplacian clusters (in case of CDL) or the tap powers for the Rayleigh fading taps (in case of TDL) are determined by

    Image,    (7.7.6-1)

where Image and Image denote the scaled and the model path power (as given in the tables) of tap/cluster Image. The model's K-factor Image is defined as

    Image.    (7.7.6-2)

After scaling the powers, the delay spread needs to be re-normalized. This is done through the two steps below.

1)    Calculate the actual RMS delay spread after the K-factor adjustment.

2)    Divide the delays by that value to obtain DS = 1.

7. 8    Channel model calibration #

7. 8.1    Large scale calibration #

For large scale calibration, fast fading is not modeled. The calibration parameters can be found in Table 7.8-1. The calibration results based on TR 38.900 V14.0.0 can be found in R1-165974.

Table 7.8-1: Simulation assumptions for large scale calibration

Parameter

Values

Scenarios

UMa, UMi-Street Canyon, Indoor-office (open office)

Sectorization

3 sectors per cell site: 30, 150 and 270 degrees

Image

BS antenna configurations

Mg = Ng = 1; (M,N,P) = (10, 1, 1), dV = 0.5λ

BS port mapping

The 10 elements are mapped to a single CRS port

BS antenna electrical downtilting

102 degrees for UMa and UMi Street Canyon

110 degrees for indoor

Antenna virtualization

DFT precoding according to TR 36.897 with application of panning and tilting angles

BS Tx power

44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz

35 dBm at 30GHz and 70 GHz for UMa and UMi-Street canyon

24 dBm for Indoor for all carrier frequencies

Bandwidth

20MHz for 6GHz, and 100MHz for 30GHz and 70 GHz

UT antenna configurations

1 element (vertically polarized), Isotropic antenna gain pattern

Handover margin (for calibration)

0dB

UT distribution

Following TR36.873 for UMa and UMi, (3D dropping)

uniform dropping for indoor with minimum distance (2D) of 0 m

UT attachment

Based on pathloss considering LOS angle

UT noise figure

9 dB

Fast fading channel

Fast fading channel is not modelled

O2I penetration loss

50% low loss and 50% high loss

Carrier Frequency

6 GHz, 30 GHz, 70GHz

Wrapping method for UMa and UMi

geographical distance based wrapping (mandatory)

radio distance (optional)

Metrics

1) Coupling loss – serving cell (based on LOS pathloss)

2) Geometry (based on LOS pathloss) with and without white noise

 

Additional calibration parameters can be found in Table 7.8-1A. It is assumed that parameters from Table 7.8-1 is used if unspecified by the additional calibration parameters in Table 7.8-1A. In addition, calibration of UMa and UMi-Street Canyon at 6 GHz carrier frequency using simulation assumptions in Table 7.8-1 with updated channel modeling is part of the additional calibration. The calibration results based on additional calibration parameters can be found in R1-2506406.

Table 7.8-1A: Simulation assumptions for large scale calibration

Parameter

Values

Scenarios

UMa, UMi-Street Canyon, SMa

Carrier Frequency

7 GHz

BS antenna downtilting

Mechanical downtilt of 95 degrees for SMa for ISD = 1299m

Mechanical downtilt of 92 degrees for SMa for ISD = 1732m

Electrical downtilt in Table 7.8-1 for UMa and UMi-Street Canyon

BS antenna configurations

Mg = Ng = 1; (M,N,P) = (10, 1, 1), dV = 0.5λ

BS port mapping

Mp = 1, Np = 1, i.e., 10 elements are mapped to a single port

Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization

BS Tx power

49 dBm for SMa

Bandwidth

20 MHz

UT distribution

For SMa, 20% of UT outdoor, 80% of UT indoor. Among indoor UT, 90% of indoor UT are within residential buildings, and 10% of indoor UT in commercial buildings. Indoor UTs are uniformly distributed across all floors for a building type.

LOS probability

For SMa, 0% vegetation

O2I penetration loss

For SMa, Low-loss A model

O2I car penetration loss

For SMa, non-metallic car window

Wrapping method

For UMa, UMi-Street Canyon, SMa:

-    geographical distance based wrapping (mandatory)

-    radio distance (optional)

 

7. 8.2    Full calibration #

The calibration parameters for full calibration including the fast fading modelling can be found in Table 7.8-2. Unspecified parameters in Table 7.8-2 are the same as those in Table 7.8-1. When P=2, X-pol (+/-45 degree) is used for BS antenna configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results based on TR 38.900 V14.0.0 can be found in R1-165975.

Table 7.8-2: Simulation assumptions for full calibration

Parameter

Values

Scenarios

UMa, UMi-street Canyon, Indoor-office (open office)

Carrier Frequency

6 GHz, 30 GHz, 60GHz, 70GHz

Bandwidth

20MHz for 6GHz, and 100MHz for 30GHz, 60 GHz and 70 GHz

BS Tx power

44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz

35 dBm at 30GHz, 60 GHz and 70 GHz for UMa and UMi-Street canyon

24 dBm for Indoor for all carrier frequencies

BS antenna configurations

Config 1: M = 4,N = 4,P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ … calibration metrics 1), 2), 3) are calibrated

Config 2: Mg = Ng = 1, M = N = 2, P = 1 … calibration metrics 1), 2), 4) are calibrated

BS port mapping

Config 1: all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations

Config 2: each antenna element is mapped to one CRS port

UT antenna configurations

Mg = Ng = 1, M = N = 1, P = 2

 

UT distribution

Following TR36.873 for UMa and UMi, (3D dropping)

uniform dropping for indoor with minimum distance (2D) of 0 m

UT attachment

Based on RSRP (formula) from CRS port 0

Polarized antenna modelling

Model-2 in TR36.873

UT orientation

ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 90 degree, ΩUT,γ = 0 degree

UT antenna pattern

Isotropic

Metrics

1) Coupling loss – serving cell

2) Wideband SIR before receiver without noise

3) CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell (according to circular angle spread definition of TR 25.996)

4) CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale.

CDF of smallest (2nd) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale.

CDF of the ratio between the largest PRB singular value and the smallest PRB singular value (serving cell) at t=0 plotted in 10*log10 scale.

See note.

NOTE:    The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB.

 

Additional full calibration parameters can be found in Table 7.8-2A. It is assumed that parameters from Table 7.8-2 is used if unspecified by the additional full calibration parameters in Table 7.8-2A. In addition, calibration of UMa and UMi-Street Canyon at 6 GHz carrier frequency using simulation assumptions in Table 7.8-2 with updated channel modeling is part of the additional calibration. For calibration of UMa and UMi-Street Canyon at 6 GHz, the following is additionally assumed.

-    SCS of 15 kHz

-    UT attachment is based on RSRP (formula) from BS port 0

-    BS antenna configuration 1 and 2 both apply

-    UT antenna configuration, pattern, and polarization modeling are labeled as UT antenna config A

The calibration results based on additional calibration parameters can be found in R1-2506406.

Table 7.8-2A: Simulation assumptions for full calibration

Parameter

Values

Scenarios

UMa, UMi-Street Canyon, SMa

Carrier Frequency

7 GHz,

(optional) 15 GHz

BS antenna downtilting

Mechanical downtilt of 95 degrees for SMa for ISD = 1299m

Mechanical downtilt of 92 degrees for SMa for ISD = 1732m

Electrical downtilt as in Table 7.8-1 for UMa and UMi-Street Canyon

BS antenna configurations

Config 3 for UMi, UMa, SMa at 7 GHz: Mg = Ng = 1, M = 8, N = 16, P = 2, dH = dV = 0.5λ … calibration metrics 1), 2), 3), 4) are calibrated

(optional) Config 4 for UMa at 7 and 15 GHz: Mg = Ng = 1, M = 64, N = 16, P = 2, Mg = 1, Ng = 1, dH = dV = 0.5λ … calibration metrics 1), 2), 3), 4) are calibrated

BS Polarized antenna modelling

Model-2 in Clause 7.3.2

BS port mapping

Config 3 for UMi, UMa, SMa at 7 GHz: Mp = 8, Np = 16, each antenna element is mapped to one port

(optional) Config 4 for UMa at 7 and 15 GHz: Mp = 16, Np = 16

Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization

BS Tx power

49 dBm for SMa

Bandwidth

20 MHz for 7 GHz

(optional) 200 MHz for 7 and 15 GHz

UT attachment

Based on RSRP (formula) from BS port 0

UT distribution

For SMa,

20% of UT outdoor, 80% of UT indoor. Among indoor UT, 90% of indoor UT are within residential buildings, and 10% of indoor UT in commercial buildings. Indoor UTs are uniformly distributed across all floors for a building type.

UT array orientation

Config B, C: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree

Config D: ΩUT,α = 0 degree, ΩUT,β= 0 degree, ΩUT,γ = 0 degree

UT antenna configurations

Config B for 7 GHz: 4 antenna port with single polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3

(optional) Config C for 15 GHz: 16 antenna port with dual polarization based on handheld device antenna model using candidate antenna locations in (1,2,3,4,5,6,7,8) as described in Clause 7.3

(only for metric 5) Config D: 8 antenna port with single polarization based on handheld device antenna model using candidate antenna locations in (1,2,3,4,5,6,7,8) as described in Clause 7.3

UT antenna pattern

Config B, C, D: Based on directional antenna for handheld UT described in Clause 7.3

UT Polarized antenna modelling

Config B, C, D: Based on directional antenna for handheld UT described in Clause 7.3

O2I penetration loss

For SMa, low-loss A model

SCS assumption

30 kHz

Additional metrics

5) Antenna field pattern of handheld UT, Fu,θ''θ',ϕ' and Fu, ϕ''(θ',ϕ'), in UT LCS using UT antenna configuration D

 

7. 8.3    Calibration of additional features #

The calibration parameters for the calibration of oxygen absorption, large bandwidth and large antenna array, spatial consistency, and blockage can be respectively found in Table 7.8-3, 7.8-4, 7.8-5, and 7.8-6. Unspecified parameters in these tables are the same as those in Tables 7.8-1 and 7.8-2. When P=2, X-pol (+/-45 degree) is used for BS antenna configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results based on TR 38.900 V14.0.0 can be found in R1-1700990.

The additional calibration parameters for the calibration of near field channel modeling, BS side spatial non-stationarity, and UT side spatial non-stationarity can be respectively found in Table 7.8-6A, 7.8-6B, and 7.8-6C. For the assumption on the aperture size of the antenna array, the following guidance is considered:

-    Up to 1.5 m for UMa with maximum antenna elements in the array is 5k for single Polarization.

-    Up to 1 m for UMi with maximum antenna elements in the array is 2.22k for single Polarization.

-    Up to 0.71 m for Indoor factory with maximum antenna elements in the array is 1.12k for single Polarization.

-    Up to 0.25 (for rectangular antenna array), 0.5 (for linear antenna array) m for Indoor office with maximum antenna elements in the array is 256, 80 for single Polarization, respectively.

The additional calibration results can be found in R1-2506406.

Table 7.8-3: Simulation assumptions for calibration for oxygen absorption

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

60 GHz

BS antenna configurations

M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ

BS port mapping

all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations

Calibration method

Drop multiple users in the multiple cells randomly, and collect the following metrics for each user after attachment.

Metrics

1)    CDF of coupling loss (serving cell)

2)    Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0

3)    CDF of Delay Spread from the serving cell

 

Table 7.8-4: Simulation assumptions for calibration for large bandwidth and large antenna array

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

30GHz

Bandwidth

2GHz

BS antenna configurations

M = 8, N = 8, P = 2, Mg = 1, Ng = 4, dH = dV = 0.5λ, dH,g = dV,g = 4λ

BS port mapping

all 64 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations

The number of rays per cluster

NMPC = 40

Calibration method

Drop multiple users in the multiple cells randomly, and collect the following metrics for each user after attachment.

Metrics

1)    CDF of coupling loss (serving cell)

2)    Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0

3)    CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale

 

Table 7.8-5: Simulation assumptions for calibration for spatial consistency

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

30 GHz

BS antenna configurations

M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ

BS port mapping

all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations

UT distribution

Following TR36.873, 3D dropping

uniform dropping for indoor with minimum distance of 0 m

For Config1: 100% UT indoor and in the 1st floor

For Config2: 100% UT outdoor

Mobility

Config1: UT is stationary

Config2: UT is moving with random direction and fixed speed, e.g., 30 km/h

Calibration method

For Config1:

Drop multiple UEs in a single cell, determine all permutations of pairs of UEs, collect the variables for each pair and bin them into certain distance ranges, e.g., 1m/2m/10m, to get enough samples. Collect the following metrics 1) –6).

For Config2:

Drop multiple users in the single cell, and collect metric 1)-2) and 7)-9) for each user after attachment.

Metrics

1)    CDF of coupling loss (serving cell)

2)    Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0

3)    Cross-correlation coefficient of delay for the third cluster between paired UEs, see note 1

4)    Cross-correlation coefficient of AOA for the third cluster between paired UEs

5)    Cross-correlation coefficient of LOS/NLOS status between paired UEs

6)    Cross correlation coefficient of the channel response in Step 11 on the first non-DC subcarrier in an OFDM symbol on antenna port 0 received on the first UT antenna over multiple realizations between paired UEs

7)    CDF of average varying rate of power for the third cluster, see note 2

8)    CDF of average varying rate of delay for the third cluster (ns)

9)    CDF of average varying rate of AOA for the third cluster (degree)

NOTE 1    For the UT pair at a certain distance range, the variables collected by two UEs can be denoted as X and Y, respectively, then the cross-correlation coefficient for real number can be written as [E(XY)-E(X)E(Y)] /sqrt([E(X^2)-E(X)^2])/ sqrt([E(Y^2)-E(Y)^2]), the cross-correlation coefficient for complex number can be written as |[E(XY*)-E(X)E(Y)*]/sqrt([E(XX*)-E(X)E(X)*])/sqrt([E(YY*)-E(Y)E(Y)*])|.

NOTE 2    For the average varying rate, we assume the collecting interval, e.g., 100ms, and then get the samples for a certain UT, the varying rate can be written as the standard variance of the samples per 100ms.

 

Table 7.8-6: Simulation assumptions for calibration for blockage

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

30 GHz

BS antenna configurations

M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ

BS port mapping

all 16 elements for each polarization on each panel are mapped to a single CRS port; panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale calibrations

Calibration method

For Model A:

Drop multiple users in the multiple cells, and collect the following metrics 1) – 3) for each user after attachment. Optional self-blocking feature is made mandatory in the Landscape mode only for calibration purposes.

For Model B:

Drop a BS in (0,0,30) and a UT in (100,0,1.5),

Jump directly to Step 11 and replace the channel with CDL-E.

Drop a blocking screen of size h=10m, w = 2m in (80,10,1.5)

Move the UT from (100,0,1.5) to (100,20,1.5) in small increments. For each UT position, translate all the AODs and AOAs of CDL-E such that the specular (LOS path) of CDL-E is pointing along the direct path between the BS and UT.

Collect metric 4)

Metrics

1)    CDF of coupling loss (serving cell)

2)    Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0

3)    CDF of ASA from the serving cell

4)    RSRP as a function of UT position

 

Table 7.8-6A: Simulation assumptions for calibration for near field channel modeling

Parameter

Values

Scenarios

UMi-street canyon, Indoor-office (open office)

Sectorization

For UMi-street canyon: sectorized as described in Table 7.8-1.

For indoor-office: non-sectorized, 1 BS per BS location.

Carrier Frequency

7 GHz

(optional) 15 GHz

Bandwidth

20 MHz

BS antenna height

UMi-street Canyon: 10m

Indoor-office: 3m

BS antenna downtilt

Electrical downtilt of 102 degrees for UMi-street canyon

Mechanical downtilt of 180 degrees for indoor-office

BS Tx power

44 dBm for UMi-street canyon

24 dBm for Indoor-office

BS antenna configurations and port mapping

For UMi-street canyon scenario:

Config 1 for 7 GHz:

(M, N, P, Mg, Ng; MP, NP) = (24, 32, 2, 1, 1; 8, 32), dH = 0.5λ, dV = 0.7λ;

(optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ

Config 2 for 15 GHz:

(M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 8, 16), dH = dV = 0.5λ

 

For Indoor-office scenario for 7 GHz:

(M, N, P, Mg, Ng; MP, NP) = (8, 24, 2, 1, 1; 8, 8), dH = dV = 0.5λ

(optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ

 

Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization

BS Polarized antenna modeling

Model-2 in Clause 7.3.2

UT Location

Outdoor/indoor

UMi-street canyon: 100% outdoor

Indoor-office: 100% indoor

LOS/NLOS

100% LOS

UT antenna height

UMi-street canyon: 1.5m

Indoor-office: 1m

UT antenna configurations

Config A: Mg = Ng = 1, M = 2, N = 1, P = 2, dH = dV = 0.5λ

Config B: 4 antenna port with single/linear polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3

UT antenna pattern

Isotropic

UT Polarized antenna modelling

For UT Config A: Model-2 in Clause 7.3.2

For UT Config B: following the clause 7.3

UT orientation

Config A: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 90 degree, ΩUT,γ = 0 degree

Config B: ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree

Applicability of Nearfield Modeling

Component of NF channel to be considered:

-    Phase of Direct path

-    Phase of Non-direct path, applied only at the BS side (optionally)

Calibration method

Drop multiple users in the multiple cells at the fixed horizontal distance Z from the BS, and collect the metric for a each distance Z

For UMi-street Canyon:

Z = 10, 30, 50, 100 m

(optional) Z = 20, 40, 80 m

Image

 

For Indoor:

Z = 0, 2, 6, 10 m

(optional) Z = 4, 8 m

Image

Metric

CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale.

NOTE 1:     The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports)

NOTE 2:     The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB.

 

Table 7.8-6B: Simulation assumptions for calibration for BS side spatial non-stationarity

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

7 GHz

(optional) 15 GHz

Bandwidth

20 MHz

BS Antenna height

10m for UMi-Street Canyon

BS Tx power

44 dBm for UMi-Street Canyon

BS Antenna Configuration and port mapping

Config 1 for 7 GHz:

(M, N, P, Mg, Ng; MP, NP) = (24, 32, 2, 1, 1; 8, 32), dH = 0.5λ, dV = 0.7λ;

(optional) (M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 16, 16), dH = dV = 0.5λ

 

Config 2 for 15 GHz:

(M, N, P, Mg, Ng; MP, NP) = (64, 16, 2, 1, 1; 8, 16), dH = dV = 0.5λ

 

Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization

BS Polarized antenna modeling

Model-2 in Clause 7.3.2

UT Antenna Configuration

Config 1: 4 antenna port with single/linear polarization for calibration based on handheld device antenna model using candidate antenna locations (1,7,3,5) as described in Clause 7.3

UT antenna pattern

Isotropic

UT Polarized antenna modelling

Following the Clause 7.3

UT orientation

ΩUT,α uniformly distributed on [0,360] degree, ΩUT,β= 45 degree, ΩUT,γ = 0 degree

UT attachment

Based on RSRP (formula) from BS port 0

UT distribution

Following TR36.873 for UMi, (3D dropping)

Spatial non-stationarity modeling at the BS

Case 1: Spatial blocker-based approach (e.g. K = 1 per sector)

Case 2: Stochastic-based approach

Metric

(1) CDF of coupling loss (serving cell).

2) CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale.

NOTE 1:     The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports)

NOTE 2:    The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB.

 

Table 7.8-6C: Simulation assumptions for calibration for UT side spatial non-stationarity

Parameter

Values

Scenarios

UMi-street Canyon

Carrier Frequency

7 GHz

(optional) 15 GHz

Bandwidth

20 MHz

BS Antenna height

10m for UMi-Street Canyon

BS Tx power

44 dBm for UMi-Street Canyon

BS Antenna Configuration and port mapping

(M, N, P, Mg, Ng; MP, NP) = (8, 16, 2, 1, 1; 8, 16), dH = dV =0.5λ

Mp and Np are the number of vertical, horizontal TXRUs within a panel and polarization

BS Polarized antenna modeling

Model-2 in Clause 7.3.2

UT Antenna Configuration

Config A for 7GHz: 4 antenna port with single polarization for calibration based on handheld device antenna model using candidate antenna locations (1, 7, 3, 5) as described in Clause 7.3

Config B for 15 GHz: 8 antenna port with single polarization based on handheld device antenna model using all candidate antenna locations (1,2,3,4,5,6,7,8) as described in Clause 7.3.

UT antenna pattern

Based on directional antenna for UT described in Clause 7.3 for each supported UT antenna configuration

UT Polarized antenna modelling

Following the Clause 7.3

UT orientation

Case with blockage:

-    For one-hand blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 45 deg, ΩUT,γ = 0 deg,

-    For dual-hand blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 0 deg, ΩUT,γ = 45 deg,

-    For hand and head blockage, ΩUT,α = 0 – 360 deg, ΩUT,β = 90 deg, ΩUT,γ = 0 deg,

Case without blockage:

-    ΩUT,α = 0 – 360 deg, ΩUT,β = 45 deg, ΩUT,γ = 0 deg,

UT attachment

Based on RSRP (formula) from BS port 0

UT distribution

Following TR36.873 for UMi, (3D dropping)

Spatial non-stationarity modeling at the UT

Case 1: one hand grip

Case 2: dual hand grip

Case 3: head and one hand grip

See note 3

Metric

CDF of coupling loss (serving cell) received per UT port

CDF of the ratio between the 2nd, 3rd ,4th, ..., xth (smallest, see note 1) PRB singular value and the 1st PRB (largest) singular value (see note 2) of the serving cell at t=0 plotted in 10*log10 scale.

NOTE 1:     The value of x is the minimum value of (number of BS antenna ports, number of UT antenna ports)

NOTE 2:    The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in the PRB.

NOTE 3:     For each UT, the selection of case is determined by the probability defined in Table 7.5.14-1. Probability of applying blockage for UT is 90%.

 

7.8.4     Calibration of the indoor factory scenario #

For the InF, the calibration parameters can be found in Table 7.8-7. The calibration results can be found in R1-1909704.

It should be noted absolute delay model had not been agreed by the deadline, so companies were not able to submit CDF of first path excess delay for serving cell.

Table 7.8-7: Simulation assumptions for large scale calibration for the indoor factory scenario

Parameter

Values

Scenario

InF-SL, InF-DL, InF-SH, InF-DH

Hall size

InF-SL: 120x60 m

InF-DL: 300x150 m

InF-SH: 300x150 m

InF-DH: 120x60 m

Room height

10 m

Sectorization

None

BS antenna configurations

1 element (vertically polarized), Isotropic antenna gain pattern

UT antenna configurations

1 element (vertically polarized), Isotropic antenna gain pattern

Handover margin (for calibration)

0dB

BS deployment

18 BSs on a square lattice with spacing D, located D/2 from the walls.

-    for the small hall (L=120m x W=60m): D=20m

-    for the big hall (L=300m x W=150m): D=50m

Image

BS height = 1.5 m for InF-SL and InF-DL

BS-height = 8 m for for InF-SH and InF-DH

UT distribution

uniform dropping for indoor with minimum 2D distance of 1 m

UT height = 1.5 m

UT attachment

Based on pathloss

UT noise figure

9 dB

Carrier frequency

3.5 GHz, 28 GHz

Bandwidth

100 MHz

Clutter density: r

Low clutter density: 20%

High clutter density: 60%

Clutter height: hc

Low clutter density: 2 m

High clutter density: 6 m

Clutter size: dclutter

Low clutter density: 10 m

High clutter density: 2 m

Metrics

1) Coupling loss – serving cell

2) Geometry with and without noise

3) CDF of delay spread and angle spread (ASD, ZSD, ASA, ZSA) according to the definition in Annex A.1

4) CDF of first path excess delay for serving cell

 

7 .9    Channel model(s) for ISAC #

7.9.0     Introduction #

The channel model for ISAC in Clause 7.9 is designed based on the channel model defined in the previous clauses within Clause 7 taking into account the known properties, e.g., location, Radar Cross-Section (RCS), polarization and etc. of one or more physical objects. A physical object is categorized as a sensing target (ST) or an environment object (EO). A ST is an object of interest for sensing. An EO is a non-target object with known location. Two types of EO are supported in the ISAC channel model. A first type of EO (type-1 EO) has similar characteristic as a ST and is modelled in the same way as a ST. In the following descriptions in Clause 7.9, only the related details on ST are described, which are also applicable to type-1 EO. A second type of EO (type-2 EO, 7.9.5.2) is of large size and is modelled differently from a ST.

The large scale and small scale parameters of the channel between any two of sensing transmitter (STX), ST and sensing receiver (SRX) in a sensing scenario are obtained from the Technical Reports for the same communication scenario unless updates on the parameter values are specially described. The ST is considered as a receiver or transmitter, respectively, in the determination of a proper channel model for a STX-ST link or a ST-SRX link.

The common framework for the ISAC channel between a pair of STX and SRX is composed of a component for one or multiple target channel(s) and a component for a background channel (described in Clause 7.9.4.3 and 7.9.5.3). One or multiple STs can be modelled in the channel between one pair of STX and SRX. The target channel of ST k includes all multipath components impacted by ST k, 1kK, where K is the number of STs. The background channel includes other multipath components not belonging to any of the K target channel(s).

7.9.1     Scenarios #

Sensing scenario X is defined as a scenario for sensing where STX/SRX are selected among the TRPs and UEs in the corresponding communication scenario X. X can be UMi/UMa/RMa/InH/InF/UMi-AV/UMa-AV/RMa-AV/Urban grid/Highway/High Speed Train (HST). Based on the interested type of ST, the sensing scenarios can be grouped to UAV sensing scenarios (ISAC-UAV), Automotive sensing scenarios (ISAC-Automotive), Human sensing scenarios (ISAC-Human), Automated Guided Vehicles (AGV) sensing scenarios (ISAC-AGV), and Objects creating hazards on roads/railways sensing scenarios (ISAC-Objects creating hazards on roads/railways).

The detailed sensing scenario description in this clause can be used for channel model calibration.

ISAC-UAV

In the ISAC-UAV sensing scenarios, the sensing targets are outdoor UAVs below or above the buildings in urban or rural areas. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other UAVs. Details on ISAC-UAV scenarios are listed in Table 7.9.1-1.

Table 7.9.1-1: Evaluation parameters for UAV sensing scenarios

Parameters

Value

Applicable communication scenarios

UMi, UMa, RMa, SMa

UMi-AV, UMa-AV, RMa-AV [36.777]

Sensing transmitters and receivers properties

STX/SRX Locations

 

STX/SRX locations are selected among the TRPs and UEs locations in the corresponding communication scenarios.

see note 1

Sensing target

LOS/NLOS

LOS and NLOS

Outdoor/indoor

Outdoor

3D mobility

Horizontal velocity: uniform distribution between 0 and 180km/h, if horizontal velocity is not fixed to 0.

 

Vertical velocity: 0km/h, optional {20, 40} km/h

see note 2 and 3

3D distribution

Horizontal plane:

Option A: N targets uniformly distributed within one cell.

Option B: N targets uniformly distributed per cell.

Option C: N targets uniformly distributed within an area not necessarily determined by cell boundaries.

N = {1, 2, 3, 4, 5}

see note 4

 

Vertical plane:

Option A: Uniform between 1.5m and 300m.

Option B: Fixed height value chosen from {25, 50, 100, 200, 300} m assuming vertical velocity is equal to 0.

Orientation

Random in horizontal domain

Physical characteristics (e.g., size)

Size:

Option 1: 1.6m x 1.5m x 0.7m

Option 2: 0.3m x 0.4m x 0.2m

Minimum 3D distances between STX/SRX and sensing target

Min distances based on min TRP/UE distances defined in TR36.777.

Minimum 3D distance between sensing targets

Option 1: At least larger than the physical size of a target

Option 2: 10 meters

NOTE 1:     This may include aerial UEs for UMi-AV, UMa-AV, RMa-AV communication scenarios. In this case, other STX/SRX properties (e.g. mobility) are also taken from the corresponding communication scenario.

NOTE 2:     3D mobility can be horizontal only or vertical only or a combination for each sensing target

NOTE 3:     time-varying velocity may be considered for future evaluations.

NOTE 4:     N=0 may be considered for the evaluation of false alarm

 

ISAC-Automotive

In the ISAC-Automotive sensing scenarios, the sensing targets are passenger vehicles or trucks and buses traveling on roads and streets in urban and rural areas. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other vehicles and roadside UEs (RSU-type UEs). Details on ISAC-Automotive scenarios are listed in Table 7.9.1-2.

Table 7.9.1-2: Evaluation parameters for Automotive sensing scenarios

Parameters

Values

Applicable communication scenarios

Highway, Urban Grid.

UMi, UMa, RMa, SMa

Sensing transmitters and receivers properties

STX/SRX locations are selected among the TRPs and UEs (e.g., VRU, vehicle, RSU-type UEs) locations in the corresponding communication scenario.

Additional option: ISD between TRPs of Urban Grid is 250m

Sensing target

LOS/NLOS

LOS and NLOS (including NLOSv)

Outdoor/indoor

Outdoor

Mobility (horizontal plane only)

Based on TR37.885 mobility for urban grid or highway scenario

Distribution (horizontal)

Based on dropping in TR37.885 per urban grid or highway communication scenario

Orientation

Lane direction in horizontal plane

Physical characteristics (e.g., size)

Type 1/2 (passenger vehicle)

Type 3 (truck/bus)

Vehicle type distribution per TR 37.885

Minimum 3D distances between STX/SRX and sensing target

For Highway, Urban Grid

-     Min distances based on min TRP/UE distances defined in TR37.885

For UMi, UMa, RMa, SMa

-     Min distances based on min TRP/UE distances defined in TR38.901

Minimum 3D distance between sensing targets

Option 1: At least larger than the physical size of a sensing target

Option 2: Fixed value, 10 m.

Environment Objects, e.g., types, characteristics, mobility, distribution, etc.

Type-2 EO for Urban Grid

-     up to 4 walls modelled as Type-2 EO, per building of size 413m x 230m x 20m.

 

ISAC-Human

In the ISAC-Human sensing scenarios, the sensing targets are children and adult persons in indoor (room, office, factory) and outdoor (urban, rural) locations. Monostatic or bistatic sensing can be performed using TRPs and/or UEs in the corresponding communication scenarios. Details on ISAC-Human scenarios are listed in Table 7.9.1-3.

Table 7.9.1-3: Evaluation parameters for Human (indoor and outdoor) sensing scenarios

Parameters

Indoor Values

Outdoor Values

Applicable communication scenarios

Indoor office, indoor factory

Indoor room [TR38.808]

UMi, UMa, RMa, SMa

Sensing transmitters and receivers properties

STX/SRX Locations

STX/SRX locations are selected among the TRPs and UE locations in the corresponding communication scenario

STX/SRX locations are selected among the TRPs and UE locations in the corresponding communication scenario

STX/SRX Mobility for UEs

Option 1: 0km/h

Option 2: 3km/h

Option 3: Uniform distribution between 0km/h and 3km/h

Option 1: 0km/h

Option 2: 3km/h

Option 3: Uniform distribution between 0km/h and 10km/h

Sensing target

LOS/NLOS

LOS and NLOS

LOS and NLOS

Outdoor/indoor

Indoor

Outdoor

3D mobility

Option 1: 0km/h

Option 2: 3km/h

Option 3: Uniform distribution between 0km/h and 3km/h

(horizontal plane with random direction straight-line trajectory)

Option 1: 0km/h

Option 2: 3km/h

Option 3: Uniform distribution between 0km/h and 10km/h

(horizontal plane with random direction straight-line trajectory)

3D distribution

N targets uniformly distributed over the horizontal area of the convex hull of the TRP deployment

see note 1

Option A: N targets uniformly distributed within one cell.

Option B: N targets uniformly distributed per cell.

Option C: N targets uniformly distributed within an area not necessarily determined by cell boundaries.

see note 1

Orientation

Random over the horizontal area

Random over the horizontal area

Physical characteristics (e.g., size)

Size (Length x Width x Height):

Child: 0.2m x 0.3m x 1m

Adult Pedestrian: 0.5m x 0.5m x 1.75m

Size (Length x Width x Height):

Child: 0.2m x 0.3m x 1m

Adult Pedestrian: 0.5m x 0.5m x 1.75m

Minimum 3D distances between STX/SRX and sensing target

For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 0 m

For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858)

For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distances between STX/SRX and the sensing target are 10 m for UMi scenarios and 35 m for UMa scenarios

For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858)

Minimum 3D distance between sensing targets

Option 1: At least larger than the physical size of a sensing target

Option 2: Fixed value, 1m

Option 1: At least larger than the physical size of a sensing target

Option 2: Fixed value, 1m

NOTE 1:     N=0 may be considered for the evaluation of false alarm

 

ISAC-AGV

In the ISAC-AGV sensing scenarios, the sensing targets are automated guided vehicles (AGVs) inside a factory. Monostatic or bistatic sensing can be performed using TRPs and/or UEs in the corresponding communication scenario. Details on ISAC-AGV sensing scenarios are listed in Table 7.9.1-4.

Table 7.9.1-4: Evaluation parameters for AGV sensing scenarios

Parameters

Value

Applicable communication scenarios

InF (Table 7.8-7)

Sensing transmitters and receivers properties

STX/SRX location are selected among the TRPs and UEs location in the corresponding communication scenario

 

STX/SRX Mobility for UEs

Option 1: 0 km/h

Option 2: 3km/h

Option 3: Uniform distribution between 0km/h and 3km/h

Sensing target

LOS/NLOS

LOS and NLOS

Outdoor/indoor

Indoor

3D mobility

Horizontal velocity with random straight-line trajectory

Option 1: Uniform distribution in the range of up to 30 km/h

Option 2: Fixed velocities {3, 10} km/h

3D distribution

Option A: Uniformly distributed in the convex hull of the horizontal BS deployment

Option B: Uniformly distributed in horizontal plane

Orientation

Horizontal plane only

Physical characteristics (e.g., size)

Size

Option 1: 0.5m x 1.0m x 0.5m

Option 2: 1.5 m x 3.0m x 1.5 m

Minimum 3D distances between STX/SRX and sensing target

Min distances based on min TRP/UE distances defined in TR38.901

Minimum 3D distance between sensing targets

Option A: At least larger than the physical size of a target

Option B: Fixed value, 5m

 

ISAC-Objects creating hazards on roads/railways

In the ISAC-Objects creating hazards on roads/railways sensing scenarios, the sensing targets are adult humans and children and animals in communication scenarios involving vehicles or high-speed trains. Monostatic or bistatic sensing can be performed using TRPs and/or UEs, including UEs on other vehicles and roadside UEs (RSU-type UEs). Details on ISAC- Objects creating hazards on roads/railways sensing scenarios are listed in Table 7.9.1-5.

Table 7.9.1-5: Evaluation parameters for objects creating hazards on roads/railways sensing scenarios

Parameters

Value

Applicable communication scenarios

Highway, Urban grid, HST (High Speed Train, TR 38.802)

UMi, UMa, RMa, SMa

Sensing transmitters and receivers properties

STX/SRX Locations

STX/SRX locations are selected among the TRPs and UEs (e.g., VRU, vehicle, RSU-type UEs) locations in the corresponding communication scenarios.

Additional option: ISD between TRPs of Urban Grid is 250 m

Sensing target

LOS/NLOS

LOS and NLOS

Outdoor/indoor

Outdoor

3D mobility

Horizontal velocity: up to 10 km/h for humans and animals

3D distribution

Uniformly distributed in horizontal plane

Orientation

Random distribution in horizontal plane

Physical characteristics (e.g., size)

For human/pedestrians: Child: 0.2m x 0.3m x 1m

Adult: 0.5m x 0.5m x 1.75m

For animals: Size: 1.5m x 0.5m x 1 m

Minimum 3D distances between STX/SRX and sensing target

For Highway, Urban Grid, HST

-     Min. distance is based on min TRP/UE distances defined in TR37.885 and TR38.802

For UMi, UMa, RMa, SMa

-     Min distances based on min TRP/UE distances defined in TR38.901

Minimum 3D distance between sensing targets

Option 1: At least larger than the physical size of a sensing target

Option 2: Fixed value, 10 m.

Environment objects, e.g., types, characteristics, mobility, distribution, etc.

Type-2 EO for Urban Grid

-     up to 4 walls modelled as Type-2 EO, per building of size 413m x 230m x 20m.

 

7.9.2     Physical object model #

7.9.2.0     Introduction #

A ST is modelled with one or multiple scattering points. Each scattering point of a ST (SPST) is used to model the total scattering effects of some adjacent scattering centres at the ST. The impact of a SPST to the channel includes at least two aspects, i.e., the RCS (Radar Cross Section, 7.9.2.1) and the polarization matrix (7.9.2.2).

7.9.2.1     RCS of a sensing target #

The RCS of a SPST is a scalar value defined in LCS of the ST and is dependent on both the incident angle and the scattered angle. The RCS values with same incident/scattered angles can be referred as monostatic RCS values.

The RCS related coefficient σRCS of a SPST for a pair of incident/scattered angles is composed of a first component σM which is included in the large-scale parameters (described in Step 15 in Clause 7.9.4.1), and a second component σD and third component σS which are both included in the small-scale parameters (described in Step 10 in Clause 7.9.4.1), i.e., σRCS=σMσDσS. σM is a deterministic value for the SPST. σD can be fixed to 1 or can be angular dependent. σS follows log-normal distribution. The mean μσS_dB and standard deviation σσS_dB used to characterize 10lgσS satisfied a fixed relation.

    μσS_dB=-ln1020σσS_dB2    (7.9.2-1)

A first RCS model, called RCS model 1, is to characterize a ST as single SPST with component σD of the monostatic RCS values fixed to 1. σM is the mean of linear monostatic RCS values at the SPST. For UAV of small size and human with RCS model 1, the values/pattern 10lgσMσD, denoted as σMD_dBθi,ϕi,θs,ϕs, of the RCS for a SPST is deterministic based on the incident angle (θi,ϕi,) and the scattered angle (θs,ϕs).

    σMD_dB(θi,ϕi,θs,ϕs)=max10lgσM-3sinβ2, σFS(θi,ϕi,θs,ϕs)     (7.9.2-2)

where,

-    β∈0°,180°. β is the bistatic angle between the incident ray and scattering ray within the plane defined by incident angle (θi,ϕi,) and scattering angle (θs,ϕs).

-    σFS(θi,ϕi,θs,ϕs) is for the effect of forward scattering and is set to -.

The logarithmic values of σM,σS of the RCS values for the different STs are provided in Table 7.9.2.1-1.

Table 7.9.2.1-1: Parameters on RCS for the STs with angular independent monostatic RCS values

Sensing target

10lgσM

(dBsm)

σσS_dB

(dB)

UAV with small size

-12.81

3.74

Human with RCS model 1

-1.37

3.94

 

A second RCS model, called RCS model 2, adopts an angular dependent component σD of the monostatic RCS values for each SPST, and is to split a ST into single or multiple SPSTs. For UAV with large size and human, single SPST is modelled. While for vehicle and AGV, both models with single and multiple SPSTs are provided. For vehicle and AGV modelled with multiple scattering points, the recommended five scattering points are located at the front, left, back, right and roof side of the vehicle respectively. The orientation of a ST in LCS is provided as follows.

-    The face of a human, the front of a vehicle, a UAV with large size or an AGV is facing the direction with azimuth angle ϕ=00 and zenith angle θ=900. The front of the AGV is the short edge of AGV in horizontal direction.

-    The top of the ST is facing the direction with zenith angle θ=00.

For UAV of large size with single SPST, human with RCS model 2 with single SPST, vehicle with single/multiple SPSTs, and AGV with single/multiple SPSTs, the values/pattern 10lgσMσD, denoted as σMD_dBθi,ϕi,θs,ϕs, of the RCS for a SPST is deterministic based on the incident angle (θi,ϕi,) and the scattered angle (θs,ϕs).

σMD_dB(θi,ϕi,θs,ϕs)=maxGmax-min-σVdBθ+σHdBϕ,σmax-k1sink2β2+5log10cosβ2, Gmax-σmax,σFS(θi,ϕi,θs,ϕs)     (7.9.2-3)

With σVdBθ, σHdBϕ defined by,

    σVdBθ=-min12θ-θcenterθ3dB2, σmax

    σHdBϕ=-min12ϕ-ϕcenterϕ3dB2, σmax

where,

-    (θ,ϕ) are zenith angle and azimuth angle of the bisector the incidence and scattered rays, whose zenith angles and azimuths are (θi,ϕi,) and (θs,ϕs).

-    β∈0°,180°. β is the bistatic angle between the incident ray and scattering ray within the plane defined by incident angle (θi,ϕi,) and scattering angle (θs,ϕs).

-    k1= 6.05, k2=1.33 for UAV with large size.

-    k1=0.5714, k2=0.1 for human with RCS model 2.

-    k1= 6, k2=1.65 for vehicle with single/multiple SPSTs.

-    k1= 12, k2=1.45 for AGV with single/multiple SPSTs.

-    σFS(θi,ϕi,θs,ϕs) is for the effect of forward scattering and is set to -.

For a ST with single scattering point, Nsp1 sets of parameters φcenter, φ3dB, θcenter, θ3dB, Gmax,σmax, Range of θ and Range of ϕ are defined. The bisector angle (θ,ϕ) is used to index one set from the Nsp sets of parameters, and determine σMD_dB(θi,ϕi,θs,ϕs) of the ST consequently. If the ST is split into Nsp scattering points, with each scattering point characterized by one of Nsp sets of parameters, σMD_dB(θi,ϕi,θs,ϕs) are respectively determined for each scattering point.

The Nsp sets of parameters to define σMσD and the parameters σM,σS of the RCS for the different STs are provided in Table 7.9.2.1-2/3/4/5/6/7.

Table 7.9.2.1-2: Parameters on RCS for UAV with large size

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Left

90

7.13

90

8.68

7.43

14.30

[45,135)

[45,135)

-5.85

2.50

Back

180

10.09

90

11.43

3.99

10.86

[45,135)

[135,225)

Right

270

7.13

90

8.68

7.43

14.30

[45,135)

[225,315)

Front

0

14.19

90

16.53

1.02

7.89

[45,135)

[-45,45)

Bottom

-

-

180

4.93

13.55

20.42

[135,180]

[0,360)

Roof

-

-

0

4.93

13.55

20.42

[0,45)

[0,360)

NOTE:     When θ is in the range [0,45] or [135,180], σHdBϕ=0.

 

Table 7.9.2.1-3: Parameters on RCS for human with RCS model 2

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Front

0

216.65

90

55.7

2.14

7.7

[0,180]

[-90, 90)

-1.37

3.94

Back

180

216.65

90

55.7

2.14

7.7

[0,180]

[90,270)

 

Table 7.9.2.1-4: Parameters on RCS for vehicle with single scattering point

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Left

90

26.90

79.70

44.42

20.75

13.68

[30,180]

[45,135)

11.25

3.41

Back

180

36.32

79.65

36.73

14.56

7.50

[30,180]

[135,225)

Right

270

26.90

79.70

44.42

20.75

13.68

[30,180]

[225,315)

Front

0

40.54

71.75

29.13

15.52

8.45

[30,180]

[-45, 45)

Roof

-

-

0.00

18.13

21.26

14.19

[0,30)

[0,360)

NOTE:     When θ is in the range [0, 30), σHdBϕ=0.

 

Table 7.9.2.1-5: Parameters on RCS for vehicle with multiple scattering points

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Left

90

26.90

79.70

44.42

20.60

20.52

[0,180]

[0,360)

11.25

3.41

Back

180

36.32

79.65

36.73

13.90

13.82

[0,180]

[0,360)

Right

270

26.90

79.70

44.42

20.60

20.52

[0,180]

[0,360)

Front

0

40.54

71.75

29.13

14.99

14.91

[0,180]

[0,360)

Roof

-

-

0.00

18.13

21.12

21.05

[0,180]

[0,360)

NOTE:     For the scattering point associated with roof of the vehicle, σHdBϕ=0.

 

Table 7.9.2.1-6: Parameters on RCS for AGV with single scattering point

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Front

0

13.68

90

13.68

13.02

23.29

[30,180]

[-45,45)

-4.25

2.51

Left

90

15.53

75

20.03

7.33

17.60

[30,180]

[45,135)

Back

180

12.49

90

11.89

11.01

21.28

[30,180]

[135,225)

Right

270

15.53

75

20.03

7.33

17.60

[30,180]

[225,315)

Roof

-

-

0

11.44

11.79

22.06

[0,30)

[0,360)

NOTE:     When θ is in the range [0,30), σHdBϕ=0.

 

Table 7.9.2.1-7: Parameters on RCS for AGV with multiple scattering points

 

10lgσMσD (dBsm)

10lgσM

(dBsm)

σσS_dB

(dB)

ϕcenter in [°]

ϕ3dB in [°]

θcenter in [°]

θ3dB in [°]

Gmax

σmax

Range of θ in [°]

Range of ϕ in [°]

Front

0

13.68

90

13.68

13.00

30.26

[0,180]

[0,360)

-4.25

2.51

Left

90

15.53

75

20.03

7.27

24.53

[0,180]

[0,360)

Back

180

12.49

90

11.89

10.98

28.24

[0,180]

[0,360)

Right

270

15.53

75

20.03

7.27

24.53

[0,180]

[0,360)

Roof

-

-

0

11.44

11.77

29.03

[0,180]

[0,360)

NOTE:     For the scattering point associated with roof of the AGV, σHdBϕ=0.

 

7.9.2.2     Cross-polarization matrix of a sensing target #

The cross-polarization matrix CPMsp,i of a SPST for a pair i of incident/scattered angles is defined in LCS of the ST and is generally modelled by amplitude factors αi,1,αi,2,βi,1,βi,2, and initial random phases Φsp,iθθ,Φsp,iθϕ,Φsp,iϕθ,Φsp,iϕϕ, i.e.,

    CPMsp,i=αi,1expjΦsp,iθθβi,1expjΦsp,iθϕβi,2expjΦsp,iϕθαi,2expjΦsp,iϕϕ    (7.9.2-4)

For UAV, human, vehicle and AGV, αi,1=αi,2=1, βi,1=βi,2=κsp,i-1, i.e.,

    CPMsp,i=expjΦsp,iθθκsp,i-1expjΦsp,iθϕκsp,i-1expjΦsp,iϕθexpjΦsp,iϕϕ    (7.9.2-5)

where,

-    κsp,i is the XPR of the pair i of incident/scattered angles. κsp,i is randomly generated by log-normal distribution per target type defined in Table 7.9.2.2-1.

-    Φsp,iθθ,Φsp,iθϕ,Φsp,iϕθ,Φsp,iϕϕ is uniformly distributed within -π,π.

Table 7.9.2.2-1: Parameters on XPR (dB) for the targets

Target

μXPR

σXPR

UAV

13.75

7.07

Human

19.81

4.25

Vehicle

21.12

6.88

AGV

9.60

6.85

 

7.9.3     Reference channel models and required updates #

A transmitter or receiver in the sensing operation can be TRP, terrestrial UE, vehicle UE, aerial UE, AGV UE or RSU-type UE. The reference TR(s) to generate the channel for each combination of transmitter and receiver for each sensing scenario are provided in Table 7.9.3-1, where the terrestrial UE, AGV are referred as terrestrial UE.

Table 7.9.3-1: Reference TRs to generate channel for ISAC

Case

Tx/Rx

Rx/Tx

Reference TR to define the channel model

1

TRP

TRP

For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF

-     TRP-TRP link of scenario UMi, UMa, InH, and InF following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

    -    For InF, hUE is changed to the same height as the BS

-     TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hUE=35m (see note 1)

For sensing scenario Highway

-     TRP-UE link of scenario RMa in Clause 7 of TR 38.901 by setting hUE=35m for FR1(see note 1)

-     TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

For sensing scenario Urban grid

-     TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

For sensing scenario HST

-     TRP-UE link of scenario RMa in Clause 7 of TR 38.901 by setting hUE=35m for FR1 (see note 1)

-     TRP-TRP link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 for FR2

2

TRP

terrestrial UE

For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF

-     TRP-UE link of scenario UMi, UMa, RMa, InH, and InF in Clause 7 of TR 38.901

For sensing scenario Highway and Urban grid

-     P2B link of scenario Highway and Urban grid in Clause 6 of TR 37.885

For sensing scenario HST

-     TRP-UE link of scenario RMa in Clause 7 of TR 38.901 for FR1 and TRP-UE link of scenario UMa in Clause 7 of TR 38.901 for FR2

3

TRP

vehicle UE

For sensing scenario Highway and Urban grid

-     V2B link of scenario Highway and Urban grid in Clause 6 of TR 37.885

For sensing scenario UMi, UMa, and RMa

-     TRP-UE link of scenario UMi, UMa, and RMa in Clause 7 of TR 38.901

4

TRP

aerial UE

For sensing scenario UMa-AV, UMi-AV, and RMa-AV

-    TRP-aerial UE link of scenario UMa-AV, UMi-AV, and RMa-AV in Clause Annex A and B of TR 36.777 for FR1

-    Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2

5

terrestrial UE

terrestrial UE

For sensing scenario UMi/UMi-AV, UMa/UMa-AV, RMa/RMa-AV, InH, InF

-     UE-UE link of scenario UMi, UMa, InH, and InF following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

-     TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m (see note 2)

For sensing scenario Highway and Urban grid

-     P2P link in Clause 6 of TR 37.885

For sensing scenario HST

-     TRP-UE link of scenario RMa in Clause 7 of TR 38.901 for FR1, e.g., hBS=1.5m, -    UE-UE link of scenario UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858 for FR2

6

terrestrial UE

vehicle UE

For sensing scenario UMi, UMa, RMa

-     UE-UE link of scenario UMi, UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

-     TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m

For sensing scenario Highway and Urban grid

-     V2P link in Clause 6 of TR 37.885

7

terrestrial UE

aerial UE

For sensing scenario UMi-AV, UMa-AV, and RMa-AV

-     TRP-aerial UE link of UMi-AV in Annex A and B of TR 36.777 by setting hBS =1.5m for FR1

-     Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2

-     The corresponding parameter values in FR2 are used

8

vehicle UE

vehicle UE

For sensing scenario Highway and Urban grid

-     V2V link of scenario Highway and Urban grid in Clause 6 of TR 37.885

For sensing scenario UMi, UMa, and RMa

-     UE-UE link of scenario UMi, UMa following the option based on TR 38.901 defined in Clause A.3 of TR 38.858

-     TRP-UE link of scenario RMa defined in Clause 7 of TR 38.901 by setting hBS =1.5m

9

aerial UE

aerial UE

For sensing scenario UMi-AV, UMa-AV, RMa-AV

-     TRP-aerial UE link of UMi-AV in Annex A and B of TR 36.777 by setting height of TRP equal to the height of the first aerial UE for FR1 (see note 4)

-     Reuse the channel model of scenario UMa-AV, UMi-AV, and RMa-AV of FR1 for FR2

-     The corresponding parameter values in FR2 are used

10

TRP

RSU-type UE

Highway and Urban grid

-    B2R link in Clause 6 of TR 37.885

11

RSU-type UE

normal UE

Highway and Urban grid

-    V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m

12

RSU-type UE

RSU-type UE

Highway and Urban grid

-    V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m

13

RSU-type UE

vehicle UE

Highway and Urban grid

-    V2V link in Clause 6 of TR 37.885, with antenna height at RSU is 5m

N OTE 1:    ASA and ZSA statistics updated to be the same as ASD and ZSD; ZoD offset = 0

N OTE 2:    ASD and ZSD statistics updated to be the same as ASA and ZSA

NOTE 3:     Indoor office scenario can be categorized into 5 sub-indoor scenarios defined in TR38.808.

NOTE 4:     First aerial UE height is hUT1, second aerial UE height is hUT2, where abshUT1-hBS≤abshUT2-hBS.

 

The large scale and small scale parameters of the STX-ST link and the ST-SRX link for a sensing scenario are determined according to Table 7.9.3-1 assuming the same sensing scenario. The targets of UAV and vehicle are respectively considered as an aerial UE and vehicle UE. Other targets are considered as terrestrial UE. The proper case for each combination of STX/STX and ST are provided in Table 7.9.3-2.

Table 7.9.3-2: Channel model for STX-ST and ST-SRX link

STX/SRX

Target

Cases in Table 7.9.3-1 assuming the same sensing scenario

TRP

UAV

Case 4: TRP-Aerial UE link

Human

Case 2: TRP-normal UE link

Vehicle

Case 3: TRP-Vehicle UE link

AGV

Case 2: TRP-normal UE link

Object (Hazard)

Case 2: TRP-normal UE link

Terrestrial UE

UAV

Case 7: normal UE - Aerial UE link

Human

Case 5: normal UE-normal UE link

Vehicle

Case 6: normal UE - Vehicle UE link

AGV

Case 5: normal UE-normal UE link

Object (Hazard)

Case 5: normal UE-normal UE link

Vehicle UE

Human

Case 6: normal UE - Vehicle UE link

Vehicle

Case 8: Vehicle UE - Vehicle UE link

Object (Hazard)

Case 6: normal UE - Vehicle UE link

Aerial UE

UAV

Case 9: Aerial UE - Aerial UE link

AGV UE

AGV

Case 5: normal UE - normal UE link

RSU-type UE

Human

Case 11: RSU-type UE - normal UE link

Vehicle

Case 13: RSU-type UE - vehicle UE link

 

The large scale and small scale parameters of the STX-SRX link, i.e., background channel for a sensing scenario are determined according to Table 7.9-1 assuming the same sensing scenario. The proper case for each combination of STX and SRX are provided in Table 7.9-3. For TRP monostatic sensing mode, a reference point (RP) is considered as a terrestrial UE. For UT monostatic sensing mode, a RP is considered as a TRP and an aerial UE is considered as a terrestrial UE.

Table 7.9.3-3: Channel model for background channel

STX/SRX

SRX/STX

Cases in Table 7.9.3-1 assuming the same sensing scenario

TRP

TRP

Case 1: TRP-TRP link

Terrestrial UE

Case 2: TRP-normal UE link

Vehicle UE

Case 3: TRP-Vehicle UE link

Aerial UE

Case 4: TRP-Aerial UE link

AGV UE

Case 2: TRP-normal UE link

RSU-type UE

Case 10: TRP - RSU-type UE link

Terrestrial UE

Terrestrial UE

Case 5: normal UE-normal UE link

Vehicle UE

Case 6: normal UE - Vehicle UE link

Aerial UE

Case 7: normal UE - Aerial UE link

AGV UE

Case 5: normal UE -normal UE link

RSU-type UE

Case 11: RSU-type UE - normal UE link

Vehicle UE

Vehicle UE

Case 8: Vehicle UE - Vehicle UE link

RSU-type UE

Case 13: RSU-type UE - vehicle UE link

Aerial UE

Aerial UE

Case 9: Aerial UE - Aerial UE link

AGV UE

AGV UE

Case 5: normal UE - normal UE link

RSU-type UE

RSU-type UE

Case 12: RSU-type UE – RSU-type UE link

 

The following updates compared to the reference TRs are identified to generate ISAC channel.

-    For human as a sensing target with a single scattering point, the height of the scattering point is 1.5 m.

-    For sensing scenario RMa, UMi-AV, UMa-AV and RMa-AV, the height of a scattering point of a target is used to calculate the LOS probability and pathloss, regardless of the lower bound of hUT in the reference TRs that are used to generate ISAC channel.

-    For sensing scenario UMi, UMa, the height of a scattering point of a target is used to calculate the LOS probability and pathloss, regardless of the lower bound of hUT in the reference TRs that are used to generate ISAC channel, except that hUT=1.5 m is assumed for breakpoint distance (dBP) calculation.

-    For sensing scenario Urban grid, the absolute delay model of scenarios UMa in Table 7.6.9-1 are reused

-    For sensing scenario highway and HST, the absolute delay model of scenarios RMa and UMa are reused for FR1 and FR2 respectively

-    For sensing scenarios UMi-AV, UMa-AV and RMa-AV,

-    For the TRP-TRP link and TRP-terrestrial UE link, the absolute delay model of scenarios UMi, UMa and RMa are respectively reused.

-    For the terrestrial UE- terrestrial UE link, the terrestrial UE - aerial UE link and the aerial UE - aerial UE link, the absolute delay model of scenario UMi is reused.

-    For the TRP- aerial UE link, the absolute delay model of scenarios UMi, UMa and RMa are respectively reused.

-    On Case 7 in Table 7.9.3-1, the LOS probability between an aerial UE and a normal UE is generated by Table 7.9.3-4. hUT is the height of the aerial UE.

-    On Case 9 in Table 7.9.3-1, the LOS probability between two aerial UEs is generated by Table 7.9.3-5. hUT1,hUT2 are the height of the two aerial UEs.

 

Table 7.9.3-4: LOS condition determination for Case 7

Reference scenario to determined LOS probability

Applicability range in terms of aerial UE height

UMi in Table 7.4.2-1 in TR 38.901 for sensing scenario UMi-AV/UMa-AV/RMa-AV

Sensing scenario UMi-AV, UMa-AV: 1.5mhUT22.5m

Sensing scenario RMa-AV: 1.5mhUT10m

UMi-AV in Table B-1 in TR 36.777 for sensing scenario UMi-AV/UMa-AV

RMa-AV in Table B-1 in TR 36.777 for sensing scenario RMa-AV

Sensing scenario UMi-AV, UMa-AV: 22.5m<hUT≤300m

Sensing scenario RMa-AV: 10m<hUT≤300m

 

Table 7.9.3-5: LOS condition determination for Case 9

Reference scenario to determined LOS probability

Applicability range in terms of aerial UE height

UMi in Table 7.4.2-1 in TR 38.901 for sensing scenario UMi-AV/UMa-AV/RMa-AV

Sensing scenario UMi-AV, UMa-AV: 1.5mhUT1,hUT222.5m

Sensing scenario RMa-AV: 1.5mhUT1,hUT210m

UMi-AV in Table B-1 in TR 36.777 for sensing scenario UMi-AV/UMa-AV

RMa-AV in Table B-1 in TR 36.777 for sensing scenario RMa-AV

Sensing scenario UMi-AV, UMa-AV: 22.5m<maxhUT1,hUT2300m, 1.5mminhUT1,hUT222.5m

Sensing scenario RMa-AV: 10m<maxhUT1,hUT2300m, 1.5mminhUT1,hUT210m

UMa-AV in Table B-1 in TR 36.777 for aerial UE height in range (22.5m, 100m] for sensing scenario UMi-AV/UMa-AV

RMa-AV in Table B-1 in TR 36.777 for aerial UE height in range (10m, 40m] for sensing scenario RMa-AV

Sensing scenario UMi-AV, UMa-AV: 22.5m<hUT1,hUT2100m

Sensing scenario RMa-AV: 10m<hUT1,hUT240m

LOS probability is 100%

Sensing scenario UMi-AV, UMa-AV: 100m<maxhUT1,hUT2300m, 22.5m<minhUT1,hUT2100m

Sensing scenario RMa-AV: 40m<maxhUT1,hUT2300m, 10m<minhUT1,hUT240m

 

7.9.4     Fast fading model #

7.9.4.0     Introduction #

The ISAC channel between a pair of STX and SRX is composed of the target channel(s) and the background channel. A multipath component in the target channel may include stochastic generated clusters in either of or both the STX-ST link and ST-SRX link. If blockage/forward scattering between sensing targets is not considered, a propagation path from STX to SRX interacting with more than one sensing target is not modelled.

A stepwise procedure illustrated in Figure 7.9.4-1 is used to generate the channel model for ISAC referring to the procedure in Clause 7.5 with parameters derived by Table 7.9.3-2 and 7.9.3-3. Step 1 is commonly executed, followed by the respective steps for target channel (7.9.4.1) and background channel (7.9.4.2). Finally, the target channel and background channel are combined to form the final channel model for ISAC (7.9.4.3).

Image

Figure 7.9.4-1 Channel coefficient generation procedure

Step 1: Set environment, network layout, and antenna array parameters

a)    Choose one of the sensing scenarios (ISAC-UAV, ISAC-Automotive, ISAC-Human, ISAC-AGV, ISAC-Objects creating hazards on roads/railways) and related communication scenarios (e.g. UMa, UMi-Street Canyon, RMa, InH-Office, Urban grid, Highway, or InF). Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors θ, ϕ as shown in Figure 7.5-2.

b)    Give number of STX, ST and SRX. Give the number of the SPST(s) of each ST.

c)    Give 3D locations of STX and SRX, give 3D locations of SPST(s) of each ST, and determine LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), and LOS ZOA (θLOS,ZOA) of each pair of STX and SPST, and each pair of SPST and SRX in the global coordinate system

d)    Give STX and SRX antenna field patterns Frx and Ftx in the global coordinate system and array geometries.

e)    Give STX and SRX array orientations with respect to the global coordinate system. STX array orientation is defined by three angles ΩSTX,α (STX bearing angle), ΩSTX,β (STX downtilt angle) and ΩSTX,γ (STX slant angle). SRX array orientation is defined by three angles ΩSRX,α (SRX bearing angle), ΩSRX,β (SRX downtilt angle) and ΩSRX,γ (SRX slant angle). Give the orientation of ST in the global coordinate system. ST orientation is defined by three angles ΩST,α (ST bearing angle), ΩST,β (ST downtilt angle) and ΩST,γ (ST slant angle).

f)    Give speed and direction of motion of STX, ST and SRX in the global coordinate system

g)    Specify system centre frequency fc and bandwidth B

NOTE:    In case wrapping is used, each wrapping copy of a STX or SRX should be treated as a separate STX/SRX considering channel generation.

For monostatic sensing mode, a STX and a corresponding SRX are co-located, and have the same antenna field pattern, array orientations, speed and direction of motion.

7.9.4.1     Target channel #

Following Step 1 in Clause 7.9.4.0, the target channels for the one or multiple STs between a pair of STX and SRX is generated using the following procedure with parameters derived by Table 7.9.3-2. It assumes a ST k consists of P scattering points indexed p=0,1,..P-1, P≥1. The propagation conditions for different STX-SPST links and SPST-SRX links can be modelled by spatial consistency procedure in Clause 7.9.5.1.

 

Large scale parameters:

Step 2: Assign propagation condition (LOS/NLOS) for each pair of STX and SPST, and each pair of SPST and SRX according to LOS probability equation defined in reference TRs in Clause 7.9.3.

 

Step 3: Calculate pathloss with formulas defined in reference TRs in Clause 7.9.3 for each STX-SPST link, and each SPST-SRX link.

 

Step 4: For each STX-SPST link and SPST-SRX link, generate large scale parameters, e.g. delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K factor (K) and shadow fading (SF) taking into account cross correlation according to Table 7.5-6 and using the procedure described in clause 3.3.1 of [14] with the square root matrixCMxM(0) being generated using the Cholesky decomposition and the following order of the large scale parameter vector: sM = [sSF, sK, sDS, sASD, sASA, sZSD, sZSA]T.

The LSPs for links from co-sited sectors to a STX/SPST/SRX are the same. In addition, these LSPs for the links of STX/SPST/SRX on different floors are uncorrelated.

Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA, 104), ASD = min(ASD, 104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA, 52), ZSD = min(ZSD, 52).

 

For monostatic sensing mode, the large scale parameters generated in step 2 to Step 4 are identical for a STX-SPST link and the corresponding SPST-SRX link of the same SPST.

 

Small scale parameters:

Step 5: Generate cluster delays

The cluster delays of cluster n in a STX-SPST link are generated using Step 5 of Clause 7.5, i.e., τtx,nk,p=τn.

The cluster delays of cluster n' in the corresponding SPST-SRX link of the same SPST are generated using Step 5 of Clause 7.5 by replacing subscript n with n’, i.e., τrx,n'k,p=τn'.

 

Step 6: Generate cluster powers.

The cluster power of cluster n in a STX-SPST link are generated using Step 6 of Clause 7.5, i.e., Ptx,nk,p=Pn.

The cluster power of cluster n' in the corresponding SPST-SRX link of the same SPST are generated using Step 6 of Clause 7.5 by replacing subscript n with n’, i.e., Prx,n'k,p=Pn'.

 

Step 7: Generate arrival angles and departure angles for both azimuth and elevation.

The arrival angles and departure angles for both azimuth and elevation of a cluster n in a STX-SPST link are generated using Step 7 of Clause 7.5, i.e., ϕtx,n,m,AOAk,p=ϕn,m,AOA, ϕtx,n,m,AODk,p=ϕn,m,AOD, θtx,n,m,ZOAk,p=θn,m,ZOA, θtx,n,m,ZODk,p=θn,m,ZOD.

The arrival angles and departure angles for both azimuth and elevation of a cluster n’ in the corresponding SPST-SRX link of the same SPST are generated using Step 7 of Clause 7.5 by replacing subscript n, m with n’, m, i.e., ϕrx,n',m',AOAk,p=ϕn',m',AOA, ϕrx,n',m',AODk,p=ϕn',m',AOD, θrx,n',m',ZOAk,p=θn',m',ZOA, θrx,n',m',ZODk,p=θn',m',ZOD.

For monostatic sensing mode, τtx,nk,p,Ptx,nk,p,ϕtx,n,m,AOAk,p,ϕtx,n,m,AODk,p,θtx,n,m,ZOAk,p,θtx,n,m,ZODk,p are respectively equal to τrx,n'k,p,Prx,n'k,p, ϕrx,n',m',AODk,p,ϕrx,n',m',AOAk,p,θrx,n',m',ZODk,p, θrx,n',m',ZOAk,p in Step 5-7, if n=n' and if applicable m=m'.

 

Step 8: Coupling of rays within a cluster for both azimuth and elevation.

The rays within a cluster n for both azimuth and elevation in a STX-SPST link are coupled using Step 8 of Clause 7.5.

The rays within a cluster n’ for both azimuth and elevation in the corresponding SPST-SRX link of the same SPST are coupled using Step 8 of Clause 7.5 by replacing subscript n, m with n’, m.

For monostatic sensing mode, the same ray-coupling way applies to both the STX-SPST link and the SPST-SRX link.

 

Step 9: Coupling of rays for a STX-SPST link and the corresponding SPST-SRX link of the same SPST.

In the STX-SPST link, a LOS ray is represented by n=0,m=0, while a NLOS ray m of a stochastic cluster n is represented by n>0,m>0. In the SPST-SRX link, a LOS ray is represented by n'=0,m'=0, while a NLOS ray m’ of a stochastic cluster n’ is represented by n'>0,m'>0. A ray in the STX-SPST link and a ray in the corresponding SPST-SRX link that are coupled to each other construct a path.

The LOS ray (if present) in the STX-SPST link is coupled with the LOS ray (if present) in the SPST-SRX link. The resulting path is never dropped. Further, a set R0 of paths are generated which include the coupled rays as follows.

-    The LOS ray in the STX-SPST link (if present) is coupled with each NLOS ray in the SPST-SRX link.

-    Each NLOS ray in the STX-SPST link is coupled with the LOS ray (if present) in the SPST-SRX link.

-    The NLOS rays in the STX-SPST link are coupled with the NLOS rays in the SPST-SRX link. Two options for the coupling are recommended with other methods for complexity reduction up to company choice.

-    Option 1: Each NLOS ray in the STX-SPST link is coupled with each NLOS ray in the SPST-SRX link.

-    Option 2: The NLOS rays in the STX-SPST link are 1-by-1 randomly coupled with the NLOS rays in the ST-SRX link. If the number of rays in the two links M1, M2 are not equal, min(M1, M2) rays are randomly selected in the link with larger number of rays in the coupling operation.

 

Step 10: Obtain the power for all generated paths

The power of a path (k,p,n',m',n,m) is given by

    Pn',m',n,mk,p=σD, n',m',n,mk,pminσS,n',m',n,mk,p,10μσσS_dB+3σσS_dB10Prx,n',m'k,pPtx,n,mk,p    (7.9.4-1)

where, σD, n',m',n,mk,p, σS,n',m',n,mk,p are the second and third component of RCS at the SPST p for a path are derived by the incident angle, i.e., AOA (ϕtx,n,m,AOAk,p), ZOA (θtx,n,m,ZOAk,p) of the ray in the STX-SPST link and the scattered angle, i.e., AOD (ϕrx,n',m',AODk,p), ZOD (θrx,n',m',ZODk,p) of the ray in the SPST-SRX link, referring to the RCS model defined in Clause 7.9.2.1 for the SPST. σS,n',m',n,mk,p is independently determined for each path of a SPST of the same ST and of the different STs. σS,n',m',n,mk,p of a path of a SPST keeps unchanged until it is updated.

NOTE:    The transformation between GCS and LCS for the incident/scattered angles is necessary to determine the RCS at the SPST p. Prx,n',m'k,p,Ptx,n,mk,p are respectively the ray power in the SPST-SRX link and the STX-SPST link.

-    If the STX-SPST link is in LOS condition,

-    for a LOS ray, Ptx,0,0k,p=Ktx,Rk,pKtx,Rk,p+1, where Ktx,Rk,p is the Ricean K-factor of the STX-SPST link.

-    for a NLOS ray m of a stochastic cluster n, Ptx,n,mk,p=1Ktx,Rk,p+1Ptx,nk,pM, where M is number of rays per cluster

-    If the STX-SPST link is not in LOS condition,

-    for a NLOS ray m of a stochastic cluster n, Ptx,n,mk,p=Ptx,nk,pM.

-    If the SPST-SRX link is in LOS condition,

-    for a LOS ray, Prx,0,0k,p=Krx,Rk,pKrx,Rk,p+1. Krx,Rk,p is the Ricean K-factor of the SPST-SRX link.

-    for a NLOS ray m’ of a stochastic cluster n’, Prx,n',m'k,p=1Krx,Rk,p+1Prx,n'k,pM.

-    If the SPST-SRX link is not in LOS condition,

-    for a NLOS ray m’ of a stochastic cluster n’, Prx,n',m'k,p=Prx,n'k,pM.

A path with power less than X dB compared to the maximum power among all paths in set R0 is dropped. The value of X can be up to -25 and -40 respectively for Option 1 and Option 2 in Step 9. The value X is up to company choice. The set of remaining paths (k,p,n',m',n,m) after path dropping is denoted as set R. The path that couples the LOS ray in the STX-SPST link and the LOS ray in the SPST-SRX link, if present, is included in set R.

 

Step 11: Obtain the absolute delay for each path in set R

The absolute delay of a path (k,p,n',m',n,m) is given by

    τn',m',n,mk,p=τrx,n',m'k,p+drx,3Dk,pc+∆τrxk,p+τtx,n,mk,p+dtx,3Dk,pc+∆τtxk,p    (7.9.4-2)

where, for the LOS ray in the STX-SPST link, if present, τtx,0,0k,p=0. For the LOS ray in the SPST-SRX link, if present, τrx,0,0k,p=0. dtx,3Dk,p is the 3D distance between STX and SPST p. drx,3Dk,p is the 3D distance between SPST p and SRX. τrxk,p,∆τtxk,p values are generated separately for NLOS rays in the SPST-SRX link and the STX-SPST link according to Table 7.6.9-1 in Clause 7.6.9. For the two strongest clusters in each of the STX-SPST link and SPST-SRX link, τrx,n',m'k,p and τtx,n,mk,p of sub-clusters are revised according to equation (7.5-26).

For monostatic sensing mode, τrxk,p is equal to τtxk,p. τrxk,p,∆τtxk,p are set to 0 for the LOS ray, if present, in the SPST-SRX link and the STX-SPST link.

 

Step 12: Generate the cross polarization power ratios for paths in set R.

The cross polarization power ratios for each ray m of a cluster n in a STX-SPST link is generated using Step 9 of Clause 7.5, i.e., κtx,n,mk,p=κn,m.

The cross polarization power ratios for each ray m’ of a cluster n’ in a SPST-SRX link is generated using Step 9 of Clause 7.5 by replacing subscript n, m with n’, m, i.e., κrx,n',m'k,p=κn',m'.

For monostatic sensing mode, κtx,n,mk,p is equal to κrx,n',m'k,p if n=n' and m=m'.

Generate the cross polarization power ratios (XPR) κn',m',m,nk,pfor each path (k,p,n',m',n,m) in set R at SPST p. XPR is log-Normal distributed. Draw XPR values as

    κn',m',m,nk,p=10Xn',m',m,nk,p/10,    (7.9.4-3)

where Xn',m',m,nk,p~N(μXPR,σXPR2) is Gaussian distributed with μXPRand σXPR from Table 7.9.2.2. Note: Xn,m is independently drawn for each path in set R. κn',m',m,nk,p is independently determined for each path of a SPST of the same ST and of the different STs. κn',m',m,nk,p of a path of a SPST keeps unchanged until it is updated.

 

The outcome of Steps 1-12 shall be identical for all the links from co-sited sectors to a STX/ST/SRX.

 

Coefficient generation:

Step 13: Draw initial random phases for paths in set R.

The random initial phases for each ray m of a cluster n in a STX-ST link is generated using Step 9 of Clause 7.5, i.e., Φtx,n,mk,p,θθ,Φtx,n,mk,p,θϕ,Φtx,n,mk,p,ϕθ,Φtx,n,mk,p,ϕϕ=Φn,mθθ,Φn,mθϕ,Φn,mϕθ,Φn,mϕϕ

The random initial phases for each ray m’ of a cluster n’ in a ST-SRX link is generated using Step 9 of Clause 7.5 by replacing subscript n, m with n’, m, i.e., Φrx,n',m'k,p,θθ,Φrx,n',m'k,p,θϕ,Φrx,n',m'k,p,ϕθ,Φrx,n',m'k,p,ϕϕ=Φn',m'θθ,Φn',m'θϕ,Φn',m'ϕθ,Φn',m'ϕϕ.

For monostatic sensing mode, Φtx,n,mk,p,θθ,Φtx,n,mk,p,θϕ,Φtx,n,mk,p,ϕθ,Φtx,n,mk,p,ϕϕ are respectively equal to Φrx,n',m'k,p,θθ, Φrx,n',m'k,p,ϕθ, Φrx,n',m'k,p,θϕ,Φrx,n',m'k,p,ϕϕ if n=n' and m=m'.

Draw random initial phases Φn',m',m,nk,p,θθ,Φn',m',m,nk,p,θϕ,Φn',m',m,nk,p,ϕθ,Φn',m',m,nk,p,ϕϕ for each path (k,p,n',m',n,m) in set R at SPST p and for four different polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-π, π). Φn',m',m,nk,p,θθ,Φn',m',m,nk,p,θϕ,Φn',m',m,nk,p,ϕθ,Φn',m',m,nk,p,ϕϕ are independently determined for each path of a SPST of the same ST and of the different STs. Φn',m',m,nk,p,θθ,Φn',m',m,nk,p,θϕ,Φn',m',m,nk,p,ϕθ,Φn',m',m,nk,p,ϕϕ of a path of a SPST keeps unchanged until it is updated.

 

Step 14: Generate channel coefficients for paths in set R for each receiver and transmitter element pair u, s.

The channel coefficient for a path (k,p,n',m',n,m) in set R is generated by

Hu,s,n',m',n,mk,pt=Pn',m',n,mk,pFrx,u,θθrx,n',m',ZOAk,p,ϕrx,n',m',AOAk,pFrx,u,ϕθrx,n',m',ZOAk,p,ϕrx,n',m',AOAk,pTCPMrx,n',m'k,pCPMn',m',n,mk,pCPMtx,n, mk,pdθθ2+dϕϕ22

Ftx,s,θθtx,n,m,ZODk,p,ϕtx,n,m,AODk,pFtx,s,ϕθtx,n,m,ZODk,p,ϕtx,n,m,AODk,pexpj2πrrx,k,p,n',m'Tt.drx,u+rtx,k,p,n,mTt.dtx,sλ0expj2πt0tfD,n',m',n,mk,ptdt    (7.9.4-4)

with the Doppler frequency fD,n',m',n,mk,pt defined as

    fD,n',m',n,mk,pt=rrx,k,p,n',m'Ttvrxt+rk,p,n',m'Ttvk,ptλ0+rtx,k,p,n,mTtvtxt+rk,p,n,mTtvk,ptλ0    (7.9.4-5)

where,

-    CPMn',m',n,mk,p is the polarization matrix of the SPST p.

    CPMn',m',n,mk,p=expjΦn',m',m,nk,p,θθκn',m',n,mk,p-1expjΦn',m',m,nk,p,θϕκn',m',n,mk,p-1expjΦn',m',m,nk,p,ϕθexpjΦn',m',m,nk,p,ϕϕ    (7.9.4-6)

CPMn,m, n',m'=k,pCPMn',m',n,mk,pTif paths (k,p,n,m,n',m') and (k,p,n',m',n,m) are in set R for mono-static sensing mod.

-    CPMtx,n, mk,p is the polarization matrix of the ray in the STX-SPST link.

-    for the LOS ray, if present, CPMtx,0, 0k,p=100-1

-    for NLOS ray generated by stochastic cluster,

    CPMtx,n, mk,p=expjΦtx,n,mk,p,θθκtx,n,mk,p-1expjΦtx,n,mk,p,θϕκtx,n,mk,p-1expjΦtx,n,mk,p,ϕθexpjΦtx,n,mk,p,ϕϕ    (7.9.4-7)

-    CPMrx,n',m'k,p is the polarization matrix of the ray in the SPST-SRX link.

-    for the LOS ray, if present, CPMrx,0,0k,p=100-1

-    for NLOS ray generated by stochastic cluster,

    CPMrx,n',m'k,p=expjΦrx,n',m'k,p,θθκrx,n',m'k,p-1expjΦrx,n',m'k,p,θϕκrx,n',m'k,p-1expjΦrx,n',m'k,p,ϕθexpjΦrx,n',m'k,p,ϕϕ    (7.9.4-8)

-    dθθ,dϕϕ are the two elements in the main diagonal of combined matrix CPMrx,n',m'k,pCPMn',m',n,mk,pCPMtx,n, mk,p

-    rrx,k,p,n',m'T is the spherical unit vector at receiver for the link from SRX to SPST p of ST k, given by

    rrx,k,p,n',m'=sinθrx,n',m',ZOAk,pcosϕrx,n',m',AOAk,psinθrx,n',m',ZOAk,psinϕrx,n',m',AOAk,pcosθrx,n',m',ZOAk,p    (7.9.4-9)

-    rtx,k,p,n,mT is the spherical unit vector at transmitter for the link from STX to SPST p of ST k, given by

    rtx,k,p,n,m=sinθtx,n,m,ZODk,pcosϕtx,n,m,AODk,psinθtx,n,m,ZODk,psinϕtx,n,m,AODk,pcosθtx,n,m,ZODk,p    (7.9.4-10)

-    rk,p,n',m'T is the spherical unit vector at the scattering point for the link from SPST p of ST k to SRX, given by

    rk,p,n',m'=sinθrx,n',m',ZODk,pcosϕrx,n',m',AODk,psinθrx,n',m',ZODk,psinϕrx,n',m',AODk,pcosθrx,n',m',ZODk,p    (7.9.4-11)

-    rk,p,n,mT is the spherical unit vector at the scattering point for the link from SPST p of ST k to STX, given by

    rk,p,n,m=sinθtx,n,m,ZOAk,pcosϕtx,n,m,AOAk,psinθtx,n,m,ZOAk,psinϕtx,n,m,AOAk,pcosθtx,n,m,ZOAk,p    (7.9.4-12)

-    vrxt,vtxt are respectively the velocity of SRX, STX.

-    vk,pt is the velocity of SPST p of ST k, vk,pt=vma,kt+vmi,k,pt, where vma,kt is the velocity of the ST k, vmi,k,pt is velocity due to micro motion of SPST p of ST k.

-    t0 denotes a reference point in time that defines the initial phase, e.g., t0=0.

 

The channel impulse response of SPST p of ST k is given by

    Hu,sk,pτ,t=n',m',n,mRHu,s,n',m',n,mk,pt δτ-τn',m',n,mk,p    (7.9.4-13)

 

Step 15: Apply pathloss, shadowing, the first component of RCS for the channel coefficients.

The target channel for ST k, Hu,s(k)τ,t is formed by summing the channel coefficients of the P SPST respectively scaled by the pathloss, shadowing and the first component of RCS σM of the P SPST.

    Hu,s(k)τ,t=p10-PLtx,k,p+PLrx,k,p+SFtx,k,p+SFrx,k,p204πσMk,pλ02Hu,s(k,p)τ,t    (7.9.4-14)

where,

-    PLtx,k,p is the pathloss of the STX-SPST link.

-    PLrx,k,p is the pathloss of the SPST-SRX link.

-    SFtx,k,p is the shadow fading of the STX-SPST link.

-    SFrx,k,p is the shadow fading of the SPST-SRX link.

-    σMk,p is the first component of RCS for the SPST p of ST k, referring to the RCS model defined in Clause 7.9.2.1.

-    p=0,1,..P-1, P is the total number of SPST of the ST k.

7.9.4.2     Background channel #

For TRP-TRP, TRP-UE, UE-TRP and UE-UE bistatic sensing modes, following Step 1 in Clause 7.9.4.0, the background channel between a pair of STX and SRX is generated using Step 2 to Step12 of Clause 7.5 with parameters derived by Table 7.9.3-3. The absolute time of arrival in clause 7.6.9 is applied. The resulting channel is denoted as Hu,sbkτ,t.

For TRP monostatic and UE monostatic sensing modes, the background channel between a pair of STX and SRX is generated using the following steps after Step 1 in Clause 7.9.4.0.

Step 2: Generate 3 reference points (RPs) for the STX/SRX.

Draw the 2D distance between the STX/SRX and each RP and the height of each RP respectively Gαd,βd+cd and Gαh,βh+ch with parameters defined in Table 7.9.4.2-1/2. Note that Gαd,βd and Gαh,βh are random values generated by the shape-rate parameterized Gamma distribution fx|α,β=βαΓ(α)xα-1ex. With uniform distribution within range -π,π, draw the LOS AOD (ϕLOS,AOD) between the STX/SRX and the first RP. The LOS AOD is further rotated by 23π and 43π to respectively derive the LOS AOD from the STX/SRX to the second and third RPs. Consequently, the 3D location of each RP can be calculated.

-    Determine LOS ZOD (θLOS,ZOD) of the STX/SRX and each RP in the global coordinate system.

-    Set each RP the same antenna field patterns Frx in the global coordinate system and array geometries as the STX/SRX.

-    Set each RP the same array orientations with respect to the global coordinate system, i.e., ΩRP,α (RP bearing angle), ΩRP,β (RP downtilt angle) and ΩRP,γ (RP slant angle) as the STX/SRX.

-    Set each RP the same velocity as the STX/SRX.

NOTE:    Distributions of height and distance of RPs are not subject to geographical constraints on TRP and UT for the corresponding deployment scenario.

 

Step 3: Assign NLOS propagation condition to the channel between the STX/SRX and each of the 3 RPs. Note: O2I is not applicable.

 

Step 4: Generate the channel between the STX/SRX and each of the 3 RPs using Step 3 to Step12 of Clause 7.5 and Clause 7.6.10 with parameters derived by Table 7.9.3-3, with updates as follows.

-    In Step 7 of Clause 7.5, the arrival angles ϕn,m,AOA,θn,m,ZOA are respectively equal to the departure angles ϕn,m,AOD, θn,m,ZOD. The rays in a cluster with θn,m,ZOA less than 50, 80 and 90 degrees respectively for scenario UMi, UMa and RMa are dropped. The dropping is not applicable to other sensing scenarios.

-    Step 8 of Clause 7.5 is not applicable, except that the random coupling of AOD angles ϕn,m,AOD with ZOD angles θn,m,ZOD within a cluster n or within a sub-cluster in the case of two strongest clusters. Apply the same coupling in the corresponding arrival angles ϕn,m,AOA,θn,m,ZOA.

-    The absolute time of arrival in clause 7.6.9 is applied with d3Dc replaced by maxd3D-cd2+(ch-hTX)2c,0, where hTX is the height of the STX/SRX. Three ∆τ are independently generated and respectively applied to the 3 channels between the STX/SRX and the 3 RPs.

NOTE:    In the UT monostatic sensing in UMa and UMi scenario, the ZOD offset should be set as 0.

 

Step 5: Generate the background channel for the STX/SRX

The background channel for the STX/SRX Hu,sbkτ,t is formed by summing the 3 channels between STX/SRX and the 3 RPs after applying the respective pathloss and shadowing.

    Hu,sbkτ,t=r=0210-PLrx,txbk,r+SFrx,txbk,r20Hu,sbk,rτ,t    (7.9.4-15)

where,

-    PLrx,txbk,r is the pathloss between the STX/SRX and the RP with index r.

-    SFrx,txbk,r is the shadow fading between the STX/SRX and the RP with index r.

 

Table 7.9.4.2-1: Parameters of background channel for TRP monostatic sensing

Sensing scenarios

UMi

UMa /

Urban grid /

Highway(FR2) /

HST(FR2)

RMa /

Highway(FR1) /

HST(FR1)

Indoor office

Indoor Factory

Distance (m)

αd

6.1996

10.3370

6.2025

4.236

0.039836

βd

0.1558

0.1317

0.0391

0.19255

0.179783

cd

15.2697

68.7778

1.2940

4.99

1.130020

Height (m)

αh

12.0487

16.2253

0.0007

1.3293

0.283447

βh

2.3261

1.9218

5.0146

0.1442

0.435965

ch

0.0157

2.6142

0.0522

-13.19

-17.043530

 

Table 7.9.4.2-2 Part-1: Parameters of background channel for UT monostatic sensing

Sensing scenarios

UMi

UMa /

Urban grid /

Highway(FR2) /

HST(FR2)

RMa /

Highway(FR1) /

HST(FR1)

Indoor office

Indoor Factory

Distance (m)

αd

10.0220

2.9072

10.2421

4.3733

0.231418

βd

1.2522

0.1031

0.0526

0.4457

0.128133

cd

11.0040

3.8471

3.3131

4.6302

2.004903

Height (m)

αh

3.0487

1.6640

0.3175

0.2974

0.462968

βh

1.9128

1.6215

1.4150

0.4103

0.281526

ch

0.1785

-1.4205

1.5906

2.9711

-16.921515

 

Table 7.9.4.2-2 Part-2: Parameters of background channel for UT monostatic sensing

Sensing scenarios

UMi-AV

UMa-AV

RMa-AV

Distance (m)

αd

0.0156h+5.5399

0.83+0.00015h

4.423+0.001926h

βd

40.4517/(h+254.6318)

1536.305+1.0279h

13.8467+0.6547h

cd

0.0140h+15.1184

13.824+0.03085h

3.864+0.1538h

Height (m)

αh

0.0123h+11.9569

0.9054-0.0001117h

1.4231+0.00192h

βh

17.8047/(h-0.2202)

138.672-0.04658h

11.7157-0.00538h

ch

0.0532h-0.0120

25.4898-0.02398h

2.6541-0.003851h

N OTE:    h is the height of the aerial UE.

 

7.9.4.3     Combining target channel and background channel #

The channel model for ISAC for a pair of STX and STX is the sum of the target channel(s) and the background channel generated in Clause 7.9.4.1 and 7.9.4.2, i.e.,

    Hu,sISACτ,t=kHu,s(k)τ,t+Hu,sbkτ,t.    (7.9.4-16)

7.9.5     Additional modelling components #

7.9.5.0     Introduction #

Similar to Clause 7.6, the additional modelling components in this clause are to support advanced simulations, such as simulations in which spatial consistency is important, simulations that include known type-2 EO, simulations that need adjusting the sum power of the target channel and the background channel, simulations of moving scatterers, simulation with impacts of lower power clusters in the background channels, and simulations of blockage effects of ST.

These extensions are computationally more expensive and might not be required in all evaluation cases.

7.9.5.1     Spatial consistency #

The spatial consistency procedure is used to generate the random variables for the STX-SPST links, the SPST-SRX links and the background channels, so that they are spatial consistent.

For sensing scenario UMi-AV, UMa-AV and RMa-AV, the 2D random process (in the horizontal plane) can be extended to a 3D random process within each applicability range in terms of aerial UE height (defined in 36.777). The correlation distance is equal to the horizontal correlation distance defined in the previous clauses within Clause 7. The random coupling of NLOS rays of Option 2 in Step 9 in Clause 7.9.4.1 shall be kept unchanged per simulation drop even if STX/ST/SRX position(s) changes during simulation.

The spatial consistency procedures in Clause 7.6.3 are reused to handle the links between TRPs and STs/UTs. RSU-type UE is considered as a TRP in the spatial consistency procedures. The spatial consistency procedures in Clause 7.6.3 applies to the links between UT and UT/SPSTs (denoted as UT-UT/SPST links), with a new correlation type for the large scale parameter, cluster specific parameters and ray specific parameters of the links.

-    Link-correlated: parameters of any two links of UT-UT/SPST links are correlated, subjected to correlation distance.

-    All-correlated: all UT-UT/SPST links are correlated.

In Table 7.9.5.1-1, the correlation type for each parameter of the new spatial consistency procedure is clarified.

Table 7.9.5.1-1: Correlation type among UT-UT/SPST links

Parameters

Correlation type

Delays

Link-correlated

Cluster powers

Link-correlated

AOA/ZOA/AOD/ZOD offset

Link-correlated

AOA/ZOA/AOD/ZOD sign

Link-correlated

Random coupling

Link-correlated

XPR

Link-correlated

Initial random phase

Link-correlated

LOS/NLOS states

Link-correlated

Blockage (Model A)

All-correlated

O2I penetration loss

All-correlated

Indoor distance

All-correlated

Indoor states

All-correlated

 

Spatial consistency is not modelled among STX-SPST links, the SPST-SRX links and the background channels in the following situations:

-    Different link types, e.g., outdoor LOS, outdoor NLOS or O2I, as defined in Table 7.6.3.4-2.

-    UE locates on different floors, as defined in Table 7.6.3.4-2.

-    Background channel for monostatic sensing mode, and any link (STX-SPST link, SPST-SRX link, background channel) for bistatic sensing mode.

-    Links associated with different non-co-located TRPs, e.g. TRP1-ST/UT/TRP and TRP2-ST/UT/TRP.

-    Links that are generated referring to channel models with parameter values of different communication scenarios.

-    The background channels for UT monostatic sensing across different UTs or across different RPs for the same UT.

-    Between TRP-target/UT link and target/UT-UT link for sensing scenario UMi, InH and InF.

-    Between TRP-TRP link and any other links for ISAC channel.

-    The background channels for TRP monostatic sensing across different TRPs or across different RPs for the same TRP.

The spatial consistency across the links between STX/SRX and multiple SPSTs of a ST are modelled as if the multiple SPSTs are multiple STs.

7.9.5.2     Type-2 environment object #

A type-2 EO, e.g., wall, is modelled as a surface with finite size. A ray specularly reflected by a type-2 EO can be modelled in the STX-SPST link, SPST-SRX link and optionally in the background channel, if a specular reflection point can be found within the surface of the type-2 EO.

A general procedure to generated small scale parameters of a type-2 EO, e.g., a wall, is provided assuming the incident ray comes from Tx and the scattered ray arrives at Rx.

Step A: Check whether a ray specularly reflected by a type-2 EO needs to be modelled or not for the pair of Tx and Rx

The type-2 EO can be described by a plane equation perpendicular to the ground.

    Ax+By=D, xxleft, xright, yyleft, yright,z[zleft, zright]    (7.9.5-1)

D is real number. [A, B, 0] is the normal vector of the plane, which points to the same direction as the orientation of the type-2 EO. The location of Tx and Rx is denoted as xtx,ytx,ztx and xrx,yrx,zrx.

The location of reflection point xw,yw,zw is determined as follows.

    xw=xtx'+xrx-xtx'(D-Axtx'-Bytx')Axrx-xtx'+B(yrx-ytx')    (7.9.5-2)

    yw=ytx'+yrx-ytx'(D-Axtx'-Bytx')Axrx-xtx'+B(yrx-ytx')    (7.9.5-3)

    zw=ztx'+zrx-ztx'(D-Axtx'-Bytx')Axrx-xtx'+B(yrx-ytx')    (7.9.5-4)

where, xtx',ytx',ztx' is the location of the reflection image of Tx relative to the plane. xtx'=xtx-2AAxtx+Bytx-DA2+B2. ytx'=ytx-2BAxtx+Bytx-DA2+B2. ztx'=ztx.

If xwxleft, xright, ywyleft, yright,zw∈[zleft, zright], the reflection point is on the surface of the type-2 EO, otherwise, there is no reflection ray in the Tx-Rx link due to the type-2 EO.

 

Step B: Generate parameters of the ray specularly reflected by a type-2 EO if the reflection point is only the surface of the type-2 EO

The 3D distance from the Tx to the reflection point then to the Rx dEO is

    dEO=(xtx'-xrx)2+(ytx'-yrx)2+(ztx'-zrx)2    (7.9.5-5)

The ZoD and AoD from Tx to the reflection point are derived based on the location of Tx and reflection point.

    θEO, ZOD=arccos⁡(zw-ztx(xw-xtx)2+(yw-ytx)2+(zw-ztx)2)    (7.9.5-6)

    ϕEO, AOD=atan2yw-ytx,xw-xtx    (7.9.5-7)

The ZoA and AoA from Rx to the reflection point are derived based on the location of Rx and reflection point.

    θEO, ZOA=arccos⁡(zw-zrx(xw-xrx)2+(yw-yrx)2+(zw-zrx)2)    (7.9.5-8)

    ϕEO, AOA=atan2yw-yrx,xw-xrx    (7.9.5-9)

The effective polarization matrix of the type-2 EO reflection ray is given by

    CPMEO=cosγ2-sinγ2sinγ2cosγ2R00-Rcosγ1sinγ1-sinγ1cosγ1    (7.9.5-10)

where,

-    γ1=atan2-eθEO, ZODnplane,eϕEO, AODnplane. nplane represents the normal vector of the incident plane. nplane=vtxw×vwrx, in which vtxw=xw-xtx,yw-ytx,zw-ztx and vwrx=xrx-xw,yrx-yw,zrx-zw. eθEO, ZOD represents the spherical basis vector of incident ray in vertical direction. eθEO,ZOD=cosθEO, ZODcosϕEO, AOD,cosθEO, ZODsinϕEO, AOD,-sinθEO, ZODT. eϕEO,AOD represents the spherical basis vector of incident ray in horizontal direction. eϕEO,AOD=-sinφEO, AOD,cosφEO, AOD,0T.

-    γ2=atan2eθEO, ZOAnplane,-eϕEO, AOAnplane. eθEO, ZOA represents the polar basis vector of scattering ray in vertical direction. eϕEO, AOA represents the spherical basis vector of scattering ray in horizontal direction. eθEO, ZOA=cosθEO, ZOAcosϕEO, AOA,cosθEO, ZOAsinϕEO, AOA,-sinθEO, ZOAT. eϕEO, AOA=-sinϕEO, AOA,cosϕEO, AOA,0T.

-    The formula 7.6-38 and 7.6-39 of reflection coefficients R// and R in Clause 7.6.8 can be reused with modified incidence angle. The incidence angle θEO is the angle between the incident vector from Tx to the reflected point and the normal vector of the type-2 EO, which is

    θEO=arccosxw-xtxA+(yw-ytx)BA2+B2(xw-xtx)2+(yw-ytx)2+(zw-ztx)2    (7.9.5-11)

    The reflection coefficients for parallel and perpendicular polarization are given by

    R//=εEOε0cosθEO+εEOε0-sin2θEOεEOε0cosθEO-εEOε0-sin2θEO    (7.9.5-12)

    R=cosθEO+εEOε0-sin2θEOcosθEO-εEOε0-sin2θEO    (7.9.5-13)

 

When a type-2 EO is deployed in the simulation area, the following modification to the ISAC channel generation in Clause 7.9.4, 7.9.4.1 and 7.9.4.2 can be applied.

1.    In Step 1 in Clause 7.9.4.0,

b)    Give number of type-2 EO, additionally.

c)    Give 3D locations of type-2 EO in the global coordinate system, additionally.

e)    Give the orientation of type-2 EO in the global coordinate system, additionally. The type-2 EO orientation is defined by three angles ΩEO,α (type-2 EO bearing angle), ΩEO,β (type-2 EO downtilt angle) and ΩEO,γ (type-2 EO slant angle).

NOTE:    In case wrapping is used, each wrapping copy of a type-2 EO should be treated as a separate type-2EO considering channel generation.

 

2.    In Step 2 in Clause 7.9.4.1,

Two options are provided to determine the LOS condition of a STX-SPST link and a SPST-SRX link.

-    Option 1: If a type-2 EO blocks the LOS ray of a link, the LOS probability of the link is equal to 0, and otherwise use the LOS probability equation defined in reference TRs in Clause 7.9.3 to determine propagation condition (LOS/NLOS) of the link.

-    Option 2: no change to Step 2 in Clause 7.9.4.1.

NOTE:    The same option is used to determine the LOS condition of a STX-SPST link, a SPST-SRX link and the background channel.

 

3.    Between Step 8 and 9 in Clause 7.9.4.1, insert one step to generate NLOS rays specularly reflected by type-2 EO, if present.

In a STX-SPST link, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the STX and Rx with the SPST. A NLOS ray specularly reflected by a type-2 EO, if present, is represented by n=0,m>0. dtx,EO,mk,p=dEO,θtx,0,m,ZOAk,p=θEO, ZOA,ϕtx,0,m,AOAk,p=ϕEO, AOA,θtx,0,m,ZODk,p=θEO, ZOD,ϕtx,0,m,AODk,p=ϕEO, AOD, CPMtx,n, mk,p=CPMEO.

In a SPST-SRX link, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the SPST and Rx with the SRX. A NLOS ray specularly reflected by a type-2 EO, if present, is represented by n'=0,m'>0. drx,EO,m'k,p=dEO,θrx,0,m',ZOAk,p=θEO, ZOA,ϕrx,0,m',AOAk,p=ϕEO, AOA,θrx,0,m',ZODk,p=θEO, ZOD,ϕrx,0,m',AODk,p=ϕEO, AOD, CPMrx,n, m'k,p=CPMEO.

 

4.    In Step 9 in Clause 7.9.4.1,

The following paths are generated and added to set R0.

-    A NLOS ray specularly reflected by type-2 EO, if present in the SPST-SRX link is coupled with a NLOS ray specularly reflected by type-2 EO, if present in the STX-SPST link

-    A NLOS ray specularly reflected by type-2 EO, if present in the SPST-SRX link is coupled with any LOS ray (if present) and NLOS rays of stochastic clusters in the STX-SPST link

-    A NLOS ray specularly reflected by type-2 EO, if present in the STX-SPST link is coupled with any LOS ray (if present) and NLOS rays of stochastic clusters in the SPST-SRX link

 

5.    In Step 10 in Clause 7.9.4.1,

Prx,0,m'k,p,Ptx,0,mk,p for a NLOS ray specularly reflected by a type-2 EO, if present, in the SPST-SRX link and the STX-SPST link is determined as follows.

-    If the STX-SPST link is in LOS condition, Ptx,0,mk,p=dtx,3Dk,pdtx,EO,mk,p2

-    If the STX-SPST link is not in LOS condition, Ptx,0,mk,p=10-PLtx,k,pLOS+PLtx,k,p/10dtx,3Dk,pdtx,EO,mk,p2, where PLtx,k,pLOS is the pathloss of STX-SPST link assuming LOS condition.

-    If the SPST-SRX link is in LOS condition, Prx,0,m'k,p=drx,3Dk,pdrx,EO,m'k,p2

-    If the SPST-SRX link is not in LOS condition, Prx,0,m'k,p=10-PLrx,k,pLOS+PLrx,k,p/10drx,3Dk,pdrx,EO,m'k,p2, where PLrx,k,pLOS is the pathloss of SPST-SRX link assuming LOS condition.

 

6.    In Step 11 in Clause 7.9.4.1,

For a NLOS ray specularly reflected by a type-2 EO in the STX-SPST link, if present, τtx,n,mk,p+dtx,3Dk,pc is replaced by dtx,EO,mk,pc, and τtxk,p is set to 0.

For a NLOS ray specularly reflected by a type-2 EO in the SPST-SRX link, if present, τrx,n',m'k,p+drx,3Dk,pc is replaced by drx,EO,m'k,pc, and τrxk,p is set to 0.

 

7.    The outcome of Steps 1-12 in Clause 7.9.4.1 shall be identical for all the links from co-sited sectors to a STX/ST/EO/SRX.

 

8.    In Clause 7.9.4.2, if a type-2 EO is modelled in the background channel between a pair of STX and SRX, the general procedure to model a type-2 EO as described above is executed by substitute Tx with the STX and Rx with the SRX.

7.9.5.3     Power normalization across target channel and background channel #

To combine the target channel and the background channel, power normalization can be applied to keep the same/similar channel power as the background channel without sensing target.

7 .9.5.4    Doppler of mobile scatterers #

To support scatterer mobility, the Doppler frequency component fD,n',m',n,mk,pt in the channel coefficient generation in step 13 in clause 7.9.4.1 should be updated as follows.

fD,n',m',n,mk,pt=rrx,k,p,n',m'Ttvrxt+rk,p,n',m'Ttvk,pt+2αrx,n',m'k,pDrx,n',m'k,pλ0+rtx,k,p,n,mTtvtxt+rk,p,n,mTtvk,pt+2αtx,n,mk,pDtx,n,mk,pλ0    (7.9.5-14)

where,

-    Drx,n',m'k,p is a random variable from -vscatt to vscatt, and vscatt is the maximum speed of the clutter. αrx,n',m'k,p is a random variable of Bernoulli distribution with mean p' if n'>0, otherwise αrx,n',m'k,p=0. Parameter p' determines the proportion of mobile scatterers and can thus be selected to appropriately model statistically larger number of mobile scatterers (higher p') or statistically smaller number of mobile scatterers (e.g. in case of a completely static environment: p'=0 results in all scatterers having zero speed).

-    Dtx,n,mk,p is a random variable from -vscatt to vscatt, and vscatt is the maximum speed of the clutter. αtx,n,mk,p is a random variable of Bernoulli distribution with mean p if n>0, otherwise αtx,n,mk,p=0. Parameter p determines the proportion of mobile scatterers and can thus be selected to appropriately model statistically larger number of mobile scatterers (higher p) or statistically smaller number of mobile scatterers (e.g. in case of a completely static environment: p=0 results in all scatterers having zero speed).

7.9.5.5     Lower power clusters #

In addition to the clusters/rays in background channel generated in Clause 7.9.4.2, more clusters/rays using NLOS propagation condition can be generated between a sensing Tx and a sensing Rx for bi-static sensing or between a sensing Tx and a reference point r, where r=0, 1, 2 for mono-static sensing to simulate the weak rays in the background channel. The following procedure is applicable for bistatic sensing or for monostatic sensing.

Step 1: Generate a first background channel Hu,sbk1τ,t for bi-static sensing and Hu,sbk1,rτ,t for reference point r for mono-static sensing according to Clause 7.9.4.2.

 

Step 2: Generate a second background channel Hu,sbk2τ,t for bi-static sensing and Hu,sbk2,rτ,t for reference point r for mono-static sensing according to Clause 7.9.4.2 including utilizing the same DS, ASA, ASD, ZSA, ZSD, Cθ, Cϕas used for the first step, if applicable, with the following revised parameters: 1) The number of clusters (N in Clause 7.5) is increased to 360; 2) The number of ray per cluster (M in Clause 7.5) is reduced to 1; 3) If LOS condition is assumed in Step 1, use NLOS condition to generate DS, ASA, ASD, ZSA, and ZSD values.

 

Step 3: The power of the clusters/rays in the second background channel is scaled down by PmaxS110G10, and is added to the first background channel, where, G=-25 dB, PmaxS1 is the maximum power of the NLOS clusters in the first background channel for bi-static sensing and PmaxS1,r is the maximum power of the NLOS clusters for reference point r in the first background channel. The final background channel Hu,sbkτ,t is 7.9.5-15a for bi-static sensing mode and 7.9.5-15b for mono-static sensing.

    Hu,sbkτ,t=Hu,sbk1τ,t+PmaxS1 10G10 Hu,sbk2τ,t    (7.9.5-15a)

    Hu,sbkτ,t=Hu,sbk1τ,t+r=02PmaxS1,r 10G10Hu,sbk2,rτ,t      (7.9.5-15b)

7.9.5.6     Blockage #

The blockage models in Clause 7.6.4 can be reused as an optional feature to model the blocking effects in the ISAC channel including the blockage due to the ST(s). The blockage of ST(s) can be applied to the LOS/NLOS rays in the background channel of the ST(s). The blockage of a first ST can be applied to the LOS/NLOS rays in the STX-ST and ST-SRX link of a second ST. The location, orientation and size of the ST as a blocker is known before applying the blockage models.

7. 9.6    Channel model calibration #

7.9.6.1     Large scale calibration #

For the purposes of large scale calibration without fast fading modelling for sensing targets UAV, human, automotive and AGV, the following calibration parameters are respectively provided in Table 7.9.6.1-1/2/3/4. Unspecified parameters in Table 7.9.6.1-2/3/4 are the same as those in Table 7.9.6.1-1. The calibration results based on TR 38.901 V19.0.0 can be found in R1-2506616.

Table 7.9.6.1-1: Simulation assumptions for large scale calibration for UAV sensing targets

Parameters

Values

Scenario

UMa-AV

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic

Target type

UAV of small size (0.3m x 0.4m x 0.2m)

Sectorization

Single 360-degree sector can be assumed

Carrier Frequency

FR1: 6 GHz

FR2: 30 GHz

BS antenna configurations

Single dual-pol isotropic antenna

BS Tx power

FR1: 56dBm

FR2: 41dBm

Bandwidth

FR1: 100MHz

FR2: 400MHz

BS noise figure

FR1: 5dB

FR2: 7dB

UT antenna configurations

Single dual-pol isotropic antenna; (M,N,P,Mg,Ng;Mp,Np) = (1,1,2,1,1;1,1)

UT noise figure

FR1: 9dB

FR2: 10dB

UT height

1.5m for terrestrial UTs,

UT Tx power

23dBm

UT Distribution

-    The overall number of UTs is 30 uniformly distributed in the center cell.

-    All of the UTs are either terrestrial UTs or aerial UTs, all outdoors.

-    Vertical distribution of aerial UE: Fixed height value of 200 m.

-    FR1 is assumed for aerial UE.

Sensing target distribution

1 target uniformly distributed (across multiple drops) within the center cell. Vertical distribution: Fixed height value of 200 m.

Component σM of the RCS for each scattering point

-12.81 dBsm

Minimum 3D distances between STX/SRX and sensing target

10 m

Wrapping Method

No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping

Coupling loss for target channel

power scaling factor (pathloss, shadow fading, and RCS component σM included):

LTX-SPST-RX=PLdBd1+PLdBd2+10lgc24πf2-10lgσRCS,A+SFdB,1+SFdB,2

STX/SRX selection

Best N = 4 STX-SRX pairs to be selected for the target.

 

see note 1

Metrics

Coupling loss for target channel

Coupling loss for background channel (in case of monostatic sensing, this is the coupling loss between Tx and one reference point)

see note 2

NOTE 1:     Based on the STX-SRX pairs with the smallest power scaling factor of the target channel.

NOTE 2:     CDFs can be separately generated for target channel, background channel.

 

Table 7.9.6.1-2. Simulation assumptions for large scale calibration for Human sensing targets

Parameters

Indoor Values

Outdoor Values

Scenario

Indoor office

12 sectors per 120m * 50m * 3m

ISD = 20m

InF-SH

Hall size: L=300 x W=150 m

Room height: 10m

18 BSs on a square lattice with spacing D, located D/2 from the walls.

- D=50m

Image

UMa (ISD = 500m), UMi (ISD= 200m)

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Adult Pedestrian: 0.5m x 0.5m x 1.75m. See note.

BS Tx power

FR1: 24dBm

FR2: 23dBm

FR1: 56dBm

FR2: 41dBm

UT height

1m

1.5m

UT Distribution

Per Table 7.8-1 Indoor-Office

Number of UTs: 20

Per Table 7.8-1.

Number of UTs/cell: 10

Sensing target distribution

100% indoor, 1 target uniformly distributed (across multiple drops) over the convex hull of the TRP deployment

100% outdoor. 1 target uniformly distributed (across multiple drops) within the center cell.

Component σM of the RCS for each scattering point

-1.37 dBsm

 

Minimum 3D distances between STX/SRX and sensing target

For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 0 m

For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858)

For TRP monostatic and TRP-TRP bistatic sensing modes, the minimum 2D distances between STX/SRX and the sensing target are 10 m for UMi scenarios and 35 m for UMa scenarios

For UE monostatic and UE-UE bistatic sensing modes, the minimum 2D distance between STX/SRX and the sensing target is 1 m (as specified in TR 38.858)

Wrapping Method

N/A

No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping

NOTE:     Height of scattering point 1.5m

 

Table 7.9.6.1-3. Simulation assumptions for large scale calibration for Automotive sensing targets

Parameters

Values

Scenario

For FR1:

-    Urban Grid (ISD=500m, BS height=25m)

-    Highway (ISD=1732m, BS height=35m)

For FR2:

-    Urban Grid (ISD=250m, BS height=25m)

-    Highway (ISD=500m, BS height=25m)

 

For Urban Grid ISD =250m

-    The layout is configured as follows:

    -    Red triangles: BS with 250m ISD, 18 BSs are located.

Image

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Vehicle type 2 [TR37.885]

UT height

1.5m for pedestrian type UE

5m for RSU type UE

1.6m for vehicle type UE

UT Distribution

For Highway:

-     Vehicle Type UT distribution follows vehicle UE dropping as in Clause 6.1.2 from TR37.885.

-     RSU-type UTs are uniformly allocated with 100m spacing in the middle of the freeway as per TR37.885.

For Urban grid:

-     Vehicle Type UT distribution follows vehicle UE dropping as in Clause 6.1.2 from TR37.885.

-     Pedestrian type UT, the dropping using equally spaced along the sidewalk with a fixed inter-pedestrian X m dropped per TR37.885

    -    Total number of pedestrian UEs is 16 in the centre grid.

    -    Pedestrian UE is in the middle of the sidewalk

    -    The inter-pedestrian UE distance (m) (i.e., X) is calculated by ‘A/(16*N)’, where ‘A’ is the total length of sidewalk where the pedestrian UEs are dropped under the assumption of ‘N’ road grids (i.e., ‘{(250m – 17m) + (433m – 17m)} * 2 * N’).

    -    N=1;

-    RSU-type UT: the dropping is at the center of intersection per TR37.885.

see note 1

Sensing target distribution

Per TR37.885:-<br>    Option A-<br>    Vehicle type distribution: 100% vehicle type 2.-<br>    Clustered dropping is not used.

-     Highway: one target uniformly distributed (across multiple drops) within the simulation region. Vehicle speed is 140 km/h in all the lanes as baseline.

-     Urban Grid: one target is uniformly distributed (across multiple drops) within the center road grid. Vehicle speed is 60 km/h in all the lanes as baseline.

 

see note 2

Component σM of the RCS for each scattering point

11.25 dBsm

 

Minimum 3D distances between STX/SRX and sensing target

Min distances based on min TRP/UE distances defined in TR37.885

Wrapping Method

No wrapping method is used if interference is not modelled, otherwise geographical distance based wrapping. As defined in urban grid/highway scenario

STX/SRX selection

Best N = 4 STX-SRX pairs to be selected for the target, with the following exceptions for Highway for FR1:

-    For TRP-TRP monostatic sensing, N=2

-    For TRP-TRP bistatic sensing, N=1

NOTE 1:     A single UT type is used per calibration, e.g., pedestrian type UT, RSU type UT, or vehicle type UTPer TR37.885

NOTE 2:     A vehicle with size L*W*H can be dropped with 5 scattering points (front/left/right/back/roof), respectively with locations (L/2,0,H/2), (0,W/2,H/2), (0,-W/2,H/2), (-L/2,0,H/2) and (0,0,H) in LCS. A vehicle with size L*W*H can be dropped with 1 scattering point with location (0,0,H/2) in LCS. In the case of vehicle with 5 scattering points, spatial consistency is enabled with the following assumptions:

    -    The correlation for LOS/NLOS condition of the 5 points is assumed equal to 1. LOS/NLOS condition can be calculated based on the distance of the STX/SRX to the centroid of the ST, then apply the LOS/NLOS condition to each of the 5 points.

    -    The correlation for stochastic cluster paths of the 5 points is assumed equal to 1. The stochastic cluster paths can be calculated between the STX/SRX and the centroid of the ST, then the stochastic cluster paths are added to each of the 5 points.

 

Table 7.9.6.1-4. Simulation assumptions for large scale calibration for AGV sensing targets

Parameters

Values

Scenario

InF: 18 TRPs per the big hall: L = 300 m x W = 150 m

Image

-    X-axis is pointing down to the floor

-    The antenna array is mounted in the Y-Z plane with boresight along the X-axis

-    The X-axis/Y-axis/Z-axis refer to LCS

-    8 m for high BS scenario

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Option 1: 0.5m x 1.0m x 0.5m

BS Tx power

FR1: 24dBm

FR2: 23dBm

UT height

1.5m

UT Distribution

Per Table 7.8-7 Indoor Factory.

Number of UTs: 30

Sensing target distribution

100% indoor, 1 target uniformly distributed (across multiple drops) over the convex hull of the TRP deployment

Component σM of the RCS for each scattering point

-4.25 dBsm

see note

NOTE:     For calibration purposes, other value(s) are not precluded,

 

7.9.6.2     Full calibration #

For the purpose of full calibration including the fast fading modelling for sensing scenarios UAV, human, automotive and AGV, the calibration parameters are respectively provided in Table 7.9.6.2-1/2/3/4. Unspecified parameters in the tables are the same as those in Table 7.9.6.1-1/2/3/4 for the same sensing target. If still not specified, the parameters in Table 7.9.6.2-1 are used. The calibration results based on TR 38.900 V19.0.0 can be found in R1-2506616.

Table 7.9.6.2-1: Simulation assumptions for full calibration for UAV sensing targets

Parameters

Values

Scenario

UMa-AV

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic

Target type

UAV of small size (0.3m x 0.4m x 0.2m)

RCS for each scattering point

Component σM: -12.81 dBsm

Component σD: 0 dB

Component σS: 3.74 dB for standard deviation

The same values are used for monostatic RCS and bistatic RCS

Fast fading model

TR 36.777 Annex B.1.3

(u, std) for XPR of target

Mean 13.75 dB, deviation 7.07 dB

The power threshold for path dropping after concatenation for target channel

-40 dB

Coupling loss for target channel

By definition, need to consider all direct and indirect paths. The following parameters are included in the calculation:

-    power scaling factor (pathloss, shadow fading, and RCS component σM included)

-     for small scale RCS σD/σS and power of rays in Tx-target/target-Rx links (Pn',m',n,mk,p), STX/SRX antenna pattern, 3 polarization matrixes, i.e.,

Pn',m',n,mk,pFrx,u,θθrx,n',m',ZOAk,p,ϕrx,n',m',AOAk,pFrx,u,ϕθrx,n',m',ZOAk,p,ϕrx,n',m',AOAk,pTCPMrx,n',m'k,pCPMn',m',n,mk,pCPMtx,n, mk,pd112+d2222

Ftx,s,θθtx,n,m,ZODk,p,ϕtx,n,m,AODk,pFtx,s,ϕθtx,n,m,ZODk,p,ϕtx,n,m,AODk,p

 

Absolute delay

The model of UMa scenario defined in Table 7.6.9-1 is reused for UMa-AV for all sensing modes.

Metrics

Coupling loss for target channel

Coupling loss for background channel (in case of monostatic sensing, this is the linear sum of coupling losses between STX/SRX and all reference points)

See note.

 

CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA). For monostatic sensing mode: delay spread and angle spread of the background channel is calculated separately for each reference point.

Definition of Delay Spread is similar to the definition of angle spread in Annex A of TR 25.996,

Definition of Angle Spread can ref to Annex A of TR 25.996.

NOTE:     CDFs can be separately generated for target channel, background channel

 

Table 7.9.6.2-2: Simulation assumptions for full calibration for Human sensing targets

Parameters

Indoor Values

Outdoor Values

Scenario

As specified in Table 7.9.6.1-2

As specified in Table 7.9.6.1-2

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Adult Pedestrian: 0.5m x 0.5m x 1.75m

see note

UT Distribution

Per Table 7.8-2 Indoor-Office

Number of UTs: 20

Per Table 7.8-2

Number of UTs/cell: 10

RCS for each scattering point

Component σM: -1.37 dBsm

Component σD: 0 dB

Component σS: 3.94 dB

The same values are used for monostatic RCS and bistatic RCS

Component σM: -1.37 dBsm

Component σD: 0 dB

Component σS: 3.94 dB

The same values are used for monostatic RCS and bistatic RCS

Fast fading model

For BS to UE link: Follow the procedure defined in TR38.901For BS to target link: Follow the procedure defined in TR38.901<br>for target to UE link: Follow the procedure defined in 38.858<br>

For BS to UE link: Follow the procedure defined in TR38.901For BS to target link: Follow the procedure defined in TR38.901<br>for target to UE link: Follow the procedure defined in 38.858<br>

(u, std) for XPR of target

(19.81, 4.25) dB

(19.81, 4.25) dB

Absolute delay

The model of InH, InF-SH scenarios defined in Table 7.6.9-1.

The model of UMa/UMi scenario defined in Table 7.6.9-1.

NOTE:     Height of scattering point 1.5m

 

Table 7.9.6.2-3: Simulation assumptions for full calibration for Automotive sensing targets

Parameters

Values

Scenario

As specified in Table 7.9.6.1-3

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Vehicle type 2 [TR37.885]

RCS for each scattering point

Based on the RCS model in Clause 7.9.2.1.

Fast fading model (full calibration)

Procedures based on 37.885

(u, std) for XPR of target

(21.12, 6.88) dB.

Absolute delay

The model of UMa scenario defined in Table 7.6.9-1 is reused for Highway (FR2)/Urban Grid for all sensing modes.

The model of RMa scenario defined in Table 7.6.9-1 is reused for Highway (1732m ISD) for all sensing modes.

 

Table 7.9.6.2-4: Simulation assumptions for full calibration for AGV sensing targets

Parameters

Values

Scenario

As specified in Table 7.9.6.1-4

Sensing mode

TRP monostatic, TRP-TRP bistatic, TRP-UE bistatic, UE-UE bistatic, UE monostatic

Target type

Option 1: 0.5m x 1.0m x 0.5m

RCS for each scattering point

The values/pattern of component σMσD are generated by Table 7.9.2.1-6

The same values are used for monostatic RCS and bistatic RCS

Fast fading model (Full calibration)

For BS to UE link: Follow the procedure defined in TR38.901

For BS to target link: Follow the procedure defined in TR38.901

for target to UE link: Follow the procedure defined in TR 38.858

(u, std) for XPR of target

(9.60, 6.85) dB

Absolute delay

The model of InF scenario defined in Table 7.6.9-1 is reused for all sensing modes.

 

7.9.6.3     Calibration of additional features #

The calibration parameters for the calibration of spatial consistency and type-2 EO can be respectively found in Table 7.9.6.3-1/2. Unspecified parameters in these tables are the same as those in Tables 7.9.6.1-2/3 and Tables 7.9.6.2-2/3. The calibration results based on TR 38.900 V19.0.0 can be found in R1-2506616.

Table 7.9.6.3-1: Simulation assumptions for calibration of spatial consistency

Parameter

Values

Scenario

Urban grid, Indoor office

Cell layout

Urban grid: ISD = 500m, the same layout with 37.885

Indoor office: Office size (WxLxH): 120mx50mx3m, ISD=20m (refer to TR 38.901)

Sensing mode

TRP- pedestrian UE bistatic in urban grid and TRP-UE bistatic in indoor office

Polarized antenna model

Model-2 in clause 7.3.2 in TR 38.901

Scattering point

Single point

Calibration link and calibration method

-    Calibration link: same target associated to different UT as following figure

Image

-    Calibration method

Step1: In single drop simulation, drop one Target and multiple UTs.

Step2: Generate the channel of each link based on Target level spatial consistency method.

Step3: Determine Target-UT groups based on the distance between UTs.

Step4: Bin all the link pairs into certain distance groups, e.g.,

    -    0m <= the location distance of link pair < 1m -> 0m group

    -    1m <= the location distance of link pair < 2m -> 1m group

    -    2m <= the location distance of link pair < 3m -> 2m group

    -    3m <= the location distance of link pair < 4m -> 3m group

    

    -     (N)m <= the location distance of pair < (N+1)m -> (N)m group (see note 2)

Step5: Calculate the correlation coefficient metric of each distance groups.

Step6: Draw the x-y cure, where x=0, …, N, y= correlation coefficient metric related to (x)m group

ST and UT distribution

-    Urban grid

one target is uniformly dropped within the center grid in the outside lane with length of 433m,

the UT is distributed by a uniform distribution distance of [0,1]m in the walk road where is in the same street with the target.

As following figure.

Image

see note 3

-    Indoor office

1 Target is uniform dropped in office region

10 UTs are dropped uniformly per cell, i.e., totally 120 UTs are uniformly dropped in office region.

Metrics

Cross-correlation coefficient of delay for the third cluster of channel between link pairs.

Cross-correlation coefficient of AOA (for UT) for the third cluster of channel between link pairs.

Cross-correlation coefficient of LOS/NLOS status of channel between link pairs(only for indoor office scenario).

NOTE 1:     Other necessary but unspecified parameters in this table are the same as those in the table for urban grid scenario calibration and indoor office scenario calibration.

NOTE 2:     N is at least twice the maximum correlation distance associated with the channel parameters in the scenario

NOTE 3:     The ST-UT link only consider LOS condition discarding NLOSv condition.

 

Table 7.9.6.3-2: Simulation assumptions for calibration of type-2 EO

Parameter

Values

Scenario

Urban grid

Cell layout

For FR1: ISD = 500m, the layout is defined as below:

The BSs are placed at the top of buildings at one corner. Specifically, the road grids shall be shifted by (ISD3-10,32*ISD3-10) m in horizontal plane, or the BSs are shifted by (-ISD3+10,-32*ISD3+10)m in horizontal plane equivalently.

Image

For FR2 ISD=250m, the cell layout is as that specified in Table 7.9.6.1-3

Sensing mode

TRP- pedestrian UE bistatic

TRP- RSU type UE bistatic

TRP monostatic

TRP-TRP bistatic

RSU type UE - RSU type UE bistatic (optional)

EO deployment

9 buildings with size 413m x 230m x 20m illustrated by the green blocks as in the figure shown in the row of cell layout.

ST distribution

one target is uniformly distributed (across multiple drops) within the center of the outside lane of the grid.

UT distribution

-    For pedestrian UT

    -    Pedestrian type UE, the dropping using equally spaced along the sidewalk with a fixed inter-pedestrian X m dropped per TR37.885.

        -    Total number of pedestrian UEs is 16 in the centre grid.

        -    Pedestrian UE is in the middle of the sidewalk

        -    The inter-pedestrian UE distance (m) (i.e., X) is calculated by ‘A/(16*N)’, where ‘A’ is the total length of sidewalk where the pedestrian UEs are dropped under the assumption of ‘N’ road grids (i.e., ‘{(250m – 17m) + (433m – 17m)} * 2 * N’).

            -    N=1;

-    For RSU type UT

    -    The dropping is at the center of intersection per TR37.885.

EM-parameters

Refer to the row for concrete in Table 7.6.8-1

Metrics

CDF curves:

-     Full coupling loss: calculate the coupling loss for each Tx-EO-ST-LOS-Rx and Tx-LOS-ST-EO-Rx rays.

-    CDF of the Delay.

-    CDF of the AoA, AoD, ZoA, ZoD.

Additional CDF curves:

-    Coupling loss for target channel

-     CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA). Definition of Delay Spread is similar to the definition of angle spread [TR 25.996, Annex A].

NOTE:     Other necessary but unspecified parameters in this table are the same as those in the table for urban grid scenario calibration.

 

8     Map-based hybrid channel model (Alternative channel model methodology) #

Map-based hybrid model is composed of a deterministic component following, e.g., METIS work [6] and a stochastic component following mainly the model described in clause 7. The channel model methodology described in this clause is an alternative to the methodology specified in clause 7, and can be used if:

-    The system performance is desired to be evaluated or predicted with the use of digital map to take into account the impacts from environmental structures and materials.

The map-based hybrid model defined in this clause is not calibrated and can be used per company basis.

8.1     Coordinate system #

The same coordinate system as defined in Clause 7.1 is applied.

8.2     Scenarios #

The same scenarios as in Clause 7.2 can be applied.

8.3     Antenna modelling #

The same antenna modelling as defined in Clause 7.3 can be applied.

8.4     Channel generation #

The radio channels are created using the deterministic ray-tracing upon a digitized map and emulating certain stochastic components according to the statistic parameters listed in Tables 7.5-6 to 7.5-11 [Note: Not all parameters listed in these tables are used in hybrid model]. The channel realizations are obtained by a step-wise procedure illustrated in Figure 8.4-1 and described below. In the following steps, downlink is assumed. For uplink, arrival and departure parameters have to be swapped.

 

Image

Figure 8.4-1: Channel coefficient generation procedure

Step-wise procedure:

Step 1: Set environment and import digitized map accordingly

a)     Choose scenario. Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors Image, Image as shown in Figure 7.5-2.

b)     Import digitized map according to the chosen scenario. The digitized map should at least contain the following information:

-    The 3D geometric information for each of major structures involving with buildings or rooms. The external building walls and internal room walls are represented by surfaces and identified by the coordinates of the vertices on each wall.

-    The material and thickness of each wall as well as the corresponding electromagnetic properties including permittivity and conductivity.

-    Random small objects in certain scenarios (e.g, UMi outdoor)

The format of digitized map, including additional information besides above-mentioned, is per implementation wise and out of scope of this description.

Step 2: Set network layout, and antenna array parameters

a)    Give number of BS and UT.

b)    Give 3D locations of BS and UT, and calculate LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA), LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system

c)    Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries

d)    Give BS and UT orientations with respect to the global coordinate system. BS array orientation is defined by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant angle). Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle if UT rotation is modelled.

e)    Give speed and direction of motion of UT in the global coordinate system for virtual motion.

f)    Give system centre frequency/frequencies and bandwidth(s) for each of BS-UT links

If the bandwidth (denoted as Image) is greater than c/D Hz, where c is the speed of light and D is the maximum antenna aperture in either azimuth or elevation, the whole bandwidth is split into Image equal-sized frequency bins, where Image is a per-implementation parameter taking into account the channel constancy as well as other potential evaluation needs, and the bandwidth of each frequency bin is Image. Within k-th frequency bin, the channel power attenuation, phase rotation, Doppler are assumed constant, whose corresponding values are calculated based on the centre frequency of k-th frequency binImage for Image, where Image is the centre frequency of the corresponding BS-UT link.

Step 3: Apply ray-tracing to each pair of link ends (i.e., end-to-end propagation between pair of Tx/Rx arrays).

a)    Perform geometric calculations in ray-tracing to identify propagation interaction types, including LOS, reflections, diffractions, penetrations and scattering (in case the digitized map contains random small objects), for each propagation path. In general, some maximum orders of different interaction types can be set.

-    The theoretical principles and procedures of geometric tracing calculations can be found in [6]~[10][12]. This description does not intend to mandate new concepts and/or procedures to the conventional ray-tracing algorithms; on the other hand, the implementation-based variations aiming to reduce computation complexity are allowed within limits of acceptable calibration tolerances.

-    The same geometric calculation is shared among all Image frequency bins.

b)    Perform electric field calculations over propagation path, based on identified propagation interaction types (LOS, reflection, diffraction, penetration and scattering) and centre frequencies of frequency bins.

    The details of electric field calculation can be found in [6]~[13].

The modelling algorithms in geometry and electric field calculations for different propagation interactions are summarized in the table below.

Table 8.4-1 Principles applied in ray-tracing

 

Geometry calculation

Electric field calculation

LOS

Free space LOS

Friis equation [11]

Reflection

Snell's law with image-based method [7]

Fresnel equation [7]

Diffraction

Fermat's principle [12]

UTD [13]

Penetration

Snell's law for transmission through slab [8]

Fresnel equation [7]

Scattering (upon small objects)

Isotropic scattering [6]

RCS-based scattering coefficient [6]

Note:    For reasons of simplicity and simulation speed, the maximum order of reflection on a path without diffraction is configurable from {1,2,3}; the maximum order of diffraction on a path without reflection is configurable from {1,2}; the path containing both reflection and diffraction has 1-order reflection and 1-order diffraction, besides any potential penetrations; and the maximum order of penetration on a path is configurable, with the recommended value equal to 5.

The outputs from Step 3 should at least contain following for each pair of link ends:

-    the LOS/NLOS flag to indicate whether a LOS propagation mechanism exists;

-    the number of deterministic propagation paths Image (also referred as deterministic clusters in Step 8. To avoid the unnecessary computation complexity, these Imagedeterministic paths only include those paths whose powers are higher than 25dB below the maximum deterministic path power, where the path power is denoted as Image and defined below);

-    for each deterministic path (Image-th path sorted in ascending order of path delay):

-    the flag indicating whether the deterministic path is generated with scattering upon random small objects;

-    the normalized path delay Image and the first arrival absolute delayImage (with Image to be the real absolute propagation delay of the path);

-    angles of arrival and departure [Image,Image,Image,Image];

-    the power Image for k-th frequency bin, and the path power Image

-    the XPR Imageof the path, where Image with Image being the XPR for k-th frequency bin.

-    to support for true motion, i.e. the case when a trajectory is specified for UT, a path ID is associated for each deterministic path. The same ID is associated for a path across a number of UT locations as far as 1) it has same interaction types in the same order and 2) its interactions occur in same walls or other surfaces.

    The Image deterministic paths are sorted by normalized path delay (Image) in ascending order. That is to say, Image= 0.

    If Image=0 for a pair of link ends, the channel gain for this pair of link ends is assumed to be zero and the remaining steps are skipped with none of random cluster.

Step 4 : Generate large scale parameters e.g. delay spread, angular spreads and Ricean K factor for random clusters.

    The generation of large scale parameters takes into account cross correlation according to Table 7.5-6 and uses the procedure described in Clause 3.3.1 of [14] with the square root matrixImagebeing generated using the Cholesky decomposition and the following order of the large scale parameter vector: sM = [sK, sDS, sASD, sASA, sZSD, sZSA]T. Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA ,104), ASD = min(ASD ,104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA = min(ZSA,52), ZSD = min(ZSD,52). For the parameter selection from Table 7.5-6, the LOS/NLOS condition determined in Step 3 is applied.

Step 5: Generate delays (denoted as {Image) for random clusters

    Delays are drawn randomly according to the exponential delay distribution

    Image    (8.4-1)

    where Image, Xn ~ uniform(0,1), and cluster index n = 0,…, Image with Image to be configurable. A recommended value for Image is the number of clusters given in Table 7.5-6.

    Image, where rτ is the delay distribution proportionality factor given in Table 7.5-6.

    Normalise the delays by subtracting the minimum delay and sort the normalised delays to ascending order:

    Image    (8.4-2)

    where Image is the additional scaling of delays to compensate for the effect of LOS peak addition to the delay spread, and is depending on the heuristically determined Ricean K-factor [dB] as generated in Step 4:

    Image    (8.4-3)

    For the delay used in cluster power generation in Step 6, the scaling factor Image is always 1.

    The n-th random cluster is removed if n=0 or Image for any of 1≤lRT≤LRT, where τth is given by Image, and Imageis the configurable probability for cluster inter-arrival interval to be less than τth. For example, set Image=0.2 to obtain τth=0.223Image.

    Denote Image for 1≤n≤LRC as the delays of the LRC random clusters that remain after the cluster removal.

As an add-on feature, the absolute time of arrival of a cluster is derived as:

-    Image, if the cluster is the Image-th deterministic path obtained in Step 3 for 1≤lRT≤LRT;

-    Image+Image, if the cluster is the n-th random cluster for 1≤n≤LRC.

 

Step 6: Generate powers (denoted as Imagefor 1≤i≤LRC) for random clusters.

    Cluster powers for the random clusters are calculated assuming a single slope exponential power delay profile. First, the virtual powers (denoted as Image for 1≤i≤LRC) of random clusters and virtual powers (denoted as Image for 1≤j≤LRT) of deterministic clusters are calculated as following.

    Denote:

    Image    (8.4-4)

    Image    (8.4-5)

    where i,RC and j,RT are the per cluster shadowing terms in [dB] and meet distribution of N(0,ζ). Then,

    Image    (8.4-6)

    Image    (8.4-7)

    In the case of LOS condition, A=KR with KR being the Ricean K-factor obtained in Step 4 and converted to linear scale; otherwise, A=0. The real power (including effects of pathloss) per random cluster in k-th frequency bin is given by

    Image    (8.4-8)

    for 1≤i≤LRC and Image. Similar to path power of deterministic cluster, the path power of i-th random cluster is calculated as

    Image.    (8.4-9)

Step 7: Generate arrival angles and departure angles for both azimuth and elevation, for each random cluster.

For azimuth angles of the n-th random cluster:

The composite PAS in azimuth of all random clusters is modelled as wrapped Gaussian (see Table 7.5-6). The AOAs are determined by applying the inverse Gaussian function with input parameters Imageand RMS angle spread ASA

    Image    (8.4-10)

with constant Image defined as    

    Image    (8.4-11)

where Image is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2.

In the LOS case, constant Image also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.

Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component Image to introduce random variation

    Image    (8.4-12)

where Imageis calculated as

    Image    (8.4-13)

Note that Image shall be given in radians here.

The generation of AOD (φn,AOD) follows a procedure similar to AOA as described above.

 

For zenith angles of the n-th random cluster:

The generation of ZOA assumes that the composite PAS in the zenith dimension of all random clusters is Laplacian (see Table 7.5-6). The ZOAs are determined by applying the inverse Laplacian function with input parameters Image and RMS angle spread ZSA

    Image    (8.4-14)

with Image defined as

    Image,    (8.4-15)

where Image is a scaling factor related to the total number of clusters and is given in Table 7.5-4.

In the LOS case, constant Image also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.

 

Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the discrete set of {1,–1}, and add component Image to introduce random variation

    Image    (8.4-16)

where Image if the UT is located indoors and Image if the UT is located outdoors, where Image is calculated as

    Image    (8.4-17)

Note that Image shall be given in radians here.

The generation of ZOD follows the same procedure as ZOA described above except equation (8.4-16) is replaced by

    Image    (8.4-18)

    where variable Xn is with uniform distribution to the discrete set of {1,–1}, Image, Imageis given in Tables 7.5-7 to 7.5-11.

Step 8: Merge deterministic clusters and random clusters.

    First, remove any deterministic or random cluster with less than -25 dB power compared to max{Image,Image} for all 1≤j≤LRT and 1≤i≤LRC. Then, simply put the remaining deterministic clusters and random clusters into single set of clusters, and meanwhile maintain an attribute for each cluster to indicate whether the cluster is a deterministic cluster or a random cluster.

Step 9: Generate ray delays and ray angle offsets inside each cluster, where the cluster can be either random or deterministic.

Denote M as the number of rays per cluster, where M=1 if the cluster corresponds to n=1 in the LOS case, otherwise the value of M is given in Table 7.5-6.

When Image:

The relative delay of m-th ray within n-th cluster is given by Image for m = 1,…,M.

The azimuth angle of arrival (AOA) for the m-th ray in n-th cluster is given by

    Image    (8.4-19)

where Image is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6, and offset angle αm is given in Table 7.5-3. Image equals to the AOA angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the AOA angle (8.4-12) in Step 7 if n-th cluster is random cluster.

 

The generation of AOD (φn,m,AOD) follows a procedure similar to AOA as described above.

The zenith angle of arrival (ZOA) for the m-th ray in n-th cluster is given by

    Image    (8.4-20)

where Image is the cluster-wise rms spread of ZOA (cluster ZOA) in Table 7.5-6, and offset angle αm is given in Table 7.5-3. Assuming that Image is wrapped within [0, 360°], if Image, thenImageis set to Image. Image equals to the ZOA angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the ZOA angle (8.4-16) in Step 7 if n-th cluster is random cluster.

The zenith angle of departure (ZOD) for the m-th ray in n-th cluster is given by

    Image    (8.4-21)

where Image is the mean of the ZSD log-normal distribution. Image equals to the ZOD angle output from Step 3 if n-th cluster is deterministic cluster, and equals to the ZOD angle (8.4-18) in Step 7 if n-th cluster is random cluster.

When Image:

The relative delay of m-th ray within n-th cluster is given by Image that are sorted in ascending order, where Image, with the cluster delay spread as given in Table 7.5-6. Image denotes the continuous uniform distribution on the interval Image. Note that Image shall be the independently generated.

The azimuth angles (AOA and AOD) and zenith angles (ZOA and ZOD) for the m-th ray in n-th cluster in each frequency bin is given by

    Image    (8.4-22)

for m = 1,…,M, where Imageand Image equal to the {AOA,AOD} and {ZOA, ZOD} angle outputs from Step 3 if n-th cluster is deterministic cluster, and equal to the {AOA,AOD} and {ZOA, ZOD} angle in Step 7 if n-th cluster is random cluster; and

    Image    (8.4-23)

with the respective cluster angular spreads as given in Tables 7.5-6 to 7.5-11.

Assuming that Imageis wrapped within [0, 360°], if Image, thenImageis set to Image.

Step 10: Generate power of rays in each cluster, where coupling of rays within a cluster for both azimuth and elevation could be needed.

Given Image as the real power in k-th frequency bin for the n-th cluster (either deterministic or random) obtained from Step 8,

When Image:

Couple randomly AOD angles φn,m,AOD to AOA angles φn,m,AOA within a cluster n. Couple randomly ZOD angles Image with ZOA angles Imageusing the same procedure. Couple randomly AOD angles φn,m,AOD with ZOD angles Imagewithin a cluster n.

The power of m-th ray in n-th cluster and in k-th frequency bin is given by Image for m = 1,…,M.

When Image:

The power of m-th ray in n-th cluster and in k-th frequency bin is given by Image for m = 1,…,M, where

    Image    (8.4-24)

and Image, Image, Image, and Image are respectively the intra-cluster delay spread and the corresponding intra-cluster angular spreads that are given in Table 7.5-6. The cluster zenith spread of departure is given by

    Image,    (8.4-25)

with Image being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.

 

Step 11: Generate XPRs

Generate the cross polarization power ratios (XPR) κfor each ray m of each cluster n. XPR is log-Normal distributed. Draw XPR values as

    Image    (8.4-26)

where Image is Gaussian distributed with Imagegiven from Table 7.5-6. If n-th cluster is a deterministic cluster, Image; otherwise, Image is given in Table 7.5-6.

Note: Image is independently drawn for each ray and each cluster.

 

Step 12: Draw initial random phases

Draw random initial phase Image for each ray m of each cluster n and for four different polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-ππ).

In the LOS case, calculate an initial phase Image for both θθ and ϕϕ polarisations, where d3D is the 3D distance between transmitter and receiver and λ0=c/fc is the wavelength of the modelled propagation link.

Step 13: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.

In case of NLOS, the channel coefficients of ray m in cluster n for a link between Rx antenna u and Tx antenna s at time t in k-th frequency bin can be calculated as

    Image    (8.4-27)

where Frx,u,θ and Frx,u,ϕ are the receive antenna element u field patterns in the direction of the spherical basis vectors, Image and Image respectively, Ftx,s,θ and Ftx,s,ϕ are the transmit antenna element s field patterns in the direction of the spherical basis vectors, Image and Image respectively. The delay (TOA) for ray m in cluster n for a link between Rx antenna u and Tx antenna s is given by:

    Image    (8.4-28)

For the m-th ray within n-th cluster, Image is the spherical unit vector with azimuth arrival angle Image and elevation arrival angle Image, given by

    Image    (8.4-29)

Image is the spherical unit vector with azimuth departure angle Image and elevation departure angle Image, given by

    Image    (8.4-30)

Also, Imageis the location vector of receive antenna element u and Imageis the location vector of transmit antenna element s, κn,m is the cross polarisation power ratio in linear scale. If polarisation is not considered, the 2x2 polarisation matrix can be replaced by the scalar Image and only vertically polarised field patterns are applied.

The Doppler frequency component is calculated from the arrival angles (AOA, ZOA), and the UT velocity vector Image with speed v, travel azimuth angle ϕv, elevation angle θv and is given by

    Image,    (8.4-31)

In case of LOS, the channel coefficient is calculated in the same way as in (8.4-27) except for n=1:

    Image    (8.4-32)

where the corresponding delay (TOA) for cluster n=1 for a link between Rx antenna u and Tx antenna s is given by Image.

In (8.4-27) and (8.4-32), the oxygen absorption loss, OLn,m(f), for each ray m in cluster n at carrier frequency f is modelled as

    OLn,m(f) = α(f)/1000 · c · Image [dB]    (8.4-33)

where:

-    α(f) is the frequency dependent oxygen loss per distance (dB/km) characterized in Clause 7.6.1;

-    c is speed of light (m/s); and

-    Image is the delay (s) obtained from Step 3 for deterministic clusters and from Step 5 for random clusters. Image is from the output of Step 3.

In (8.4-27) and (8.4-32), blockage modelling is an add-on feature. If the blockage model is applied, the blockage loss, BLn,m(f,t) in unit of dB, for each ray m in cluster n at carrier frequency f and time t is modelled in the same way as given in Clause 7.6.4; otherwise BLn,m(f,t)=0dB for all f and t.

<br>Annex A: Further parameter definitions<br> #

A.1     Calculation of angular spread #

Based on the circular standard deviation in directional statistics, the following expression for the angular spread AS in radians is proposed

    AS=-2lnn=1Nm=1Mexpjφn,mPn,mn=1Nm=1MPn,m    (A-1)

where Pn,m is the power for the mth subpath of the nth path and φn,m is the subpaths angle (either AOA, AOD, ZOA, ZOD) given in radians.

A.2     Calculation of mean angle #

The power weighted mean angle is given by

    μφ=argn=1Nm=1Mexpjφn,mPn,m    (A-2)

where Pn,m is the power for the mth subpath of the nth path and φn,m is the subpaths angle (either AOA, AOD, ZOA, ZOD) given in radians.

A.3     Calculation of cluster angular spread #

The cluster angular spread in degrees is given by

    AS=180π-2lnexp180φLOSPLOS+n=1Nexp180φnPnPLOS+n=1NPn    (A-3)

where Pn is the power for the nth cluster path, φn is the cluster path angle (either AOA, AOD, ZOA, ZOD) given in degrees, PLOS is the power for the LOS path, and φLOS is the LOS path angle (either AOA, AOD, ZOA, ZOD) given in degrees. If LOS path does not exist, PLOS=0 is assumed.

A.4     Calculation of cluster mean angle #

The power weighted cluster mean angle in degrees is given by

    μφ=180πargexp180φLOSPLOS+n=1Nexp180φnPn    (A-4)

where arg{z} is the angle in radians measured from the positive real axis to the line joining the origin and complex value z, Pn is the power for the nth cluster path, φn is the cluster path angle (either AOA, AOD, ZOA, ZOD) given in degrees, PLOS is the power for the LOS path, and φLOS is the LOS path angle (either AOA, AOD, ZOA, ZOD) given in degrees. If LOS path does not exist, PLOS=0 is assumed.

A.5     Calculation of scaling factor for changing CDL model angular spread #

The following expression for the computing scaling factor, s, to achieve a specific angular spread, AS, in degrees is given by

    s=minx≥0{x: ASx=ASdesired}    (A-5)

    ASx=180π-2lnexpjxπ180WrapTo180(φLOS)PLOS+n=1Nexpjxπ180WrapTo180(φn)PnPLOS+n=1NPn    (A-6)

where Pn is the power for the nth cluster path, φn is the input cluster path angle (either AOA, AOD, ZOA, ZOD) given in degrees, PLOS is the power for the input LOS path, and φLOS is the input LOS path angle (either AOA, AOD, ZOA, ZOD) given in degrees. If input LOS path does not exist, PLOS=0 is assumed.

 

<br>Annex B: Change history #

 

Change history

Date

Meeting

TDoc

CR

Rev

Cat

Subject/Comment

New version

2017-02

RAN1#88

R1-1703641

 

 

 

Skeleton TR based on TR 38.900 v14.2.0, adding new changes (from R1-1701231, R1-1701406, R1-1701410, R1-1701411, R1-1701412, R1-1701413, R1-1701414, R1-1701416, R1-1701417 and R1-1701425) to have a TR applicable for the frequency range 0.5 to 100 GHz

0.0.1

2017-02

RAN1#88

R1-1704145

 

 

 

Adding new agreements (from R1-1702701, R1-1703635, R1-1703873, R1-1703358, R1-1702703, R1-1703637, R1-1703354, R1-1703458, R1-1703647, R1-1701936) and reflecting comments from RAN1 e-mail discussions [88-17].

0.1.0

2017-02

RAN#75

RP-170394

 

 

 

Submitted to RAN for information

1.0.0

2017-03

RAN#75

RP-170737

 

 

 

Add TR number further to the approval of revised SID in RP-170379. Submitted to RAN for one step approval

1.0.1

2017-03

RAN#75

 

 

 

 

MCC clean-up – Further to RAN#75 decision, TR goes under change control as Rel-14

14.0.0

2017-06

RAN#76

RP-171208

0001

-

F

Correction on site specific effective height for TR38.901

14.1.0

2017-06

RAN#76

RP-171208

0003

-

F

Correction for LOS probability

14.1.0

2017-06

RAN#76

RP-171208

0004

-

F

Correction for scaling factor for ZOD generation

14.1.0

2017-06

RAN#76

RP-171208

0005

-

F

Correction for modelling of intra-cluster angular and delay spreads

14.1.0

2017-06

RAN#76

RP-171208

0007

-

F

TR38.901_CR_LCS_Radiation_Field_Notation

14.1.0

2017-06

RAN#76

RP-171208

0008

-

F

TR38.901_CR_Mean_angle_definition

14.1.0

2017-06

RAN#76

RP-171208

0009

-

F

TR38.901_CR_O2I_penetration_loss

14.1.0

2017-06

RAN#76

RP-171208

0010

1

F

TR38.901_CR_Correction_on_Rural_LSP

14.1.0

2017-06

RAN#76

RP-171208

0011

1

F

Clarification for spatial consistency procedure

14.1.0

2017-06

RAN#76

RP-171208

0013

-

F

TR38.901_CR_Scenarios_in_Simulation_Assumptions

14.1.0

2017-06

RAN#76

RP-171208

0014

-

F

TR38.901_CR_TDL_Spatial_Filter

14.1.0

2017-06

RAN#76

RP-171208

0015

-

F

Correction of spatial-consistency UE mobility modelling Procedure A

14.1.0

2017-07

 

 

 

 

 

MCC: UMa LOS probability in Table 7.4-2 is made fully visible

14.1.1

2017-09

RAN#77

RP-171648

0016

-

F

Correction on the formula of Annex A

14.2.0

2017-09

RAN#77

RP-171648

0018

2

F

Correction on cross polarization power ratios in TR 38.901

14.2.0

2017-09

RAN#77

RP-171648

0019

1

F

TR38.901_CR_Spatially-consistent_UT_modelling

14.2.0

2017-09

RAN#77

RP-171648

0020

1

F

TR38.901_CR_Calibraiton_Results

14.2.0

2017-12

RAN#78

RP-172687

0022

-

F

Correction on 3D-InH channel model in TR 38.901

14.3.0

2018-06

SA#80

-

-

-

-

Update to Rel-15 version (MCC)

15.0.0

2019-09

RAN#85

RP-191944

0023

-

B

Addition of indoor industrial channel model – version created in error – withdrawn

15.1.0

2019-10

RAN#85

RP-191944

0023

-

B

Addition of indoor industrial channel model – creation of rel-16 report

16.0.0

2019-12

RAN#86

RP-192629

0024

-

F

CR to TR 38.901 for remaining open issues in IIOT channel modelling

16.1.0

2022-03

SA#95-e

 

 

 

 

Update to Rel-17 version (MCC)

17.0.0

2023-12

RAN#102

RP-233727

0025

-

F

CR correcting scaling of angles for CDL model and a figure reference

17.1.0

2024-03

SA#103

-

-

-

-

Update to Rel-18 version (MCC)

18.0.0

2025-06

RAN#108

RP-251566

0026

-

B

Introduction of Rel-19 7-24 GHz channel model enhancements

19.0.0

2025-06

RAN#108

RP-251567

0027

-

B

CR to introduce channel model for ISAC

19.0.0

2025-09

RAN#109

RP-252622

0028

-

F

Correction of channel modeling enhancements for 7 - 24 GHz

19.1.0

2025-09

RAN#109

RP-252623

0029

-

F

CR to incorporate new agreements on ISAC CM

19.1.0